Modelling with Dynamic Clustering KNN

import pandas as pd
import numpy as np

dataset = pd.read_csv('Modelling Dataset.csv')

df_upcoming = dataset[dataset['source'] == 'upcoming'].copy()
df_upcoming = df_upcoming.drop(columns =["Unnamed: 0"])

dataset = dataset[dataset['source'] == 'historical'].copy()

# Save the upcoming set separately (for final model deployment)
df_upcoming.to_csv("upcoming_preprocessed.csv", index=False)

print(dataset.shape)
dataset.head()

(4360, 39)

	Unnamed: 0	RedFighter_ID	BlueFighter_ID	RedFighter	BlueFighter	Winner	source	Skill_Diff	BlueFighter_Skill	RedFighter_Skill	...	BlueTotalRoundsFought	TotalRoundDif	BlueAvgTDPct	AgeDif	TotalFightTimeSecs	RedAvgSubAtt	ReachDif	BlueAvgSubAtt	RedAvgSubAtt.1	RedReachCms
11	11	60	885	Alexandre Pantoja	Kai Asakura	0.0	historical	2.042789	-0.664030	1.907847	...	-0.993020	-1.969865	-1.388762	-0.598588	-0.610082	0.386986	0.603432	-0.760181	0.386986	-1.465363
12	12	1446	634	Shavkat Rakhmonov	Ian Machado Garry	0.0	historical	0.520062	2.100826	2.758269	...	0.410035	0.803575	0.997108	-0.598588	2.314757	1.892772	-0.850206	-0.305855	1.892772	1.109244
13	13	309	57	Ciryl Gane	Alexander Volkov	0.0	historical	0.274928	1.284732	1.632491	...	2.093700	0.912337	1.344144	0.362980	0.682289	-0.064750	-0.268751	-0.457297	-0.064750	2.139087
14	14	210	935	Bryce Mitchell	Kron Gracie	0.0	historical	1.971959	-1.552982	0.928641	...	-0.501951	-0.501573	-0.304275	1.132235	-0.027835	1.591615	0.021976	-0.002971	1.591615	-0.692981
15	15	1180	466	Nate Landwehr	Dooho Choi	1.0	historical	-0.043829	1.015199	0.961201	...	0.059271	0.205382	0.216278	-0.598588	0.412931	0.688143	-0.559479	0.451354	0.688143	-0.178060

5 rows × 39 columns

X = dataset.drop(columns=["Winner", "RedFighter_ID", "RedFighter", "BlueFighter", "BlueFighter_ID", "Unnamed: 0",'source'])
y = dataset["Winner"]

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

print((y == 0).sum()) # Red Winners
print((y == 1).sum()) # Blue Winners

2513
1847

SMOTE for oversampling under represented Blue class

from imblearn.over_sampling import SMOTE

sm = SMOTE(random_state=42)
X_resampled, y_resampled = sm.fit_resample(X_train, y_train)

/Users/avkar/PycharmProjects/FinalYearProject/venv/lib/python3.9/site-packages/sklearn/base.py:474: FutureWarning: `BaseEstimator._validate_data` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation.validate_data` instead. This function becomes public and is part of the scikit-learn developer API.
  warnings.warn(

import matplotlib.pyplot as plt
import seaborn as sns

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

# Before SMOTE
sns.countplot(x=y_train, ax=axes[0], palette="pastel")
axes[0].set_title("Class Distribution Before SMOTE")
axes[0].set_xlabel("Winner")
axes[0].set_ylabel("Count")
axes[0].set_xticklabels(["Red Win (0)", "Blue Win (1)"])

# After SMOTE
sns.countplot(x=y_resampled, ax=axes[1], palette="pastel")
axes[1].set_title("Class Distribution After SMOTE")
axes[1].set_xlabel("Winner")
axes[1].set_ylabel("Count")
axes[1].set_xticklabels(["Red Win (0)", "Blue Win (1)"])
plt.tight_layout()
plt.show()

/var/folders/g7/d5vdjz6s1vl8ksk5tqs00s_c0000gn/T/ipykernel_29831/1493853714.py:7: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x=y_train, ax=axes[0], palette="pastel")
/var/folders/g7/d5vdjz6s1vl8ksk5tqs00s_c0000gn/T/ipykernel_29831/1493853714.py:11: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  axes[0].set_xticklabels(["Red Win (0)", "Blue Win (1)"])
/var/folders/g7/d5vdjz6s1vl8ksk5tqs00s_c0000gn/T/ipykernel_29831/1493853714.py:14: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x=y_resampled, ax=axes[1], palette="pastel")
/var/folders/g7/d5vdjz6s1vl8ksk5tqs00s_c0000gn/T/ipykernel_29831/1493853714.py:18: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  axes[1].set_xticklabels(["Red Win (0)", "Blue Win (1)"])

Dimensionality reduction with t-SNE v PCA v UMAP

t-SNE

Parmater Tuning

from sklearn.manifold import TSNE

# Define parameters to test
perplexities = [5, 15, 30, 50]
learning_rates = [10, 200, 500]

fig, axs = plt.subplots(len(perplexities), len(learning_rates), figsize=(20, 15))

for i, perplexity in enumerate(perplexities):
    for j, learning_rate in enumerate(learning_rates):
        tsne = TSNE(
            n_components=2, 
            perplexity=perplexity,
            learning_rate=learning_rate,
            early_exaggeration=12,  
            random_state=42,
            n_iter=1000,
            verbose=0
        )
        X_tsne = tsne.fit_transform(X_train)  

        sns.scatterplot(
            x=X_tsne[:, 0], y=X_tsne[:, 1],
            hue=y_train,
            palette={0: "red", 1: "blue"},
            ax=axs[i, j],
            s=40,
            legend=False
        )
        axs[i, j].set_title(f"Perp={perplexity}, LR={learning_rate}")
        axs[i, j].set_xlabel("t-SNE 1")
        axs[i, j].set_ylabel("t-SNE 2")

plt.tight_layout()
plt.show()

from sklearn.manifold import TSNE

tsne = TSNE(n_components=2, random_state=42, perplexity=30, n_iter=1000)
X_tsne = tsne.fit_transform(X_train)  

/Users/avkar/PycharmProjects/FinalYearProject/venv/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:1164: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.
  warnings.warn(

PCA

from sklearn.decomposition import PCA
pca = PCA(n_components=2, random_state=42)
X_pca = pca.fit_transform(X_train)  

UMAP

Parameter Tuning

import umap

# Define parameter grid
n_neighbors_list = [5, 15, 30, 50]
min_dist_list = [0.01, 0.1, 0.5, 0.9]

# Set up 4x4 grid
fig, axes = plt.subplots(len(n_neighbors_list), len(min_dist_list), figsize=(20, 20))
fig.suptitle("UMAP Cluster Projections — Tuning n_neighbors and min_dist", fontsize=24)

# Loop through each param combo
for i, n_nbrs in enumerate(n_neighbors_list):
    for j, min_d in enumerate(min_dist_list):
        reducer = umap.UMAP(
            n_components=2,
            n_neighbors=n_nbrs,
            min_dist=min_d,
            random_state=42
        )
        X_umap_proj = reducer.fit_transform(X_train)

        ax = axes[i, j]
        sns.scatterplot(
            x=X_umap_proj[:, 0],
            y=X_umap_proj[:, 1],
            hue=y_train,
            palette={0: "red", 1: "blue"},  # Red = Class 0, Blue = Class 1
            ax=ax,
            s=40,
            alpha=0.7,
            edgecolor=None,
            legend=False
        )
        ax.set_title(f"n_neighbors={n_nbrs}, min_dist={min_d}", fontsize=12)
        ax.set_xlabel("UMAP 1")
        ax.set_ylabel("UMAP 2")

plt.tight_layout(rect=[0, 0, 1, 0.97])
plt.show()

Testing PCA, UMAP & t-SNE with KNN

from umap import UMAP
from sklearn.manifold import TSNE

umap_model = UMAP(n_components=2, n_neighbors=5, min_dist=0.01, random_state=42)
X_train_umap = umap_model.fit_transform(X_train)
X_test_umap = umap_model.transform(X_test)

pca_model = PCA(n_components=2)
X_train_pca = pca_model.fit_transform(X_train)
X_test_pca = pca_model.transform(X_test)

tsne_model = TSNE(n_components=2, random_state=42, perplexity=15, learning_rate=500, n_iter=1000)
X_train_tsne = tsne_model.fit_transform(X_train)  

/Users/avkar/PycharmProjects/FinalYearProject/venv/lib/python3.9/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.
  warnings.warn(
/Users/avkar/PycharmProjects/FinalYearProject/venv/lib/python3.9/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.
  warn(
/Users/avkar/PycharmProjects/FinalYearProject/venv/lib/python3.9/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.
  warnings.warn(
/Users/avkar/PycharmProjects/FinalYearProject/venv/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:1164: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.
  warnings.warn(

from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report

# KNN classifier
def evaluate_reduction_techniques(X_train_reduced, y_train, X_test_reduced, y_test, n_neighbours):
    acc_list = {}
    for n in n_neighbours:
        knn = KNeighborsClassifier(n)
        knn.fit(X_train_reduced, y_train)
        y_pred = knn.predict(X_test_reduced)
        acc_list[n] = accuracy_score(y_test, y_pred)

    return acc_list

n_neighbours = list(range(3, 20, 2))
umap_acc_list = evaluate_reduction_techniques(X_train_umap, y_train, X_test_umap, y_test, n_neighbours)
pca_acc_list = evaluate_reduction_techniques(X_train_pca, y_train, X_test_pca, y_test, n_neighbours)

import matplotlib.pyplot as plt


plt.figure(figsize=(10, 6))
plt.style.use('seaborn-v0_8')

# Extract values in correct order
neighbors = list(pca_acc_list.keys())
pca_scores = list(pca_acc_list.values())
umap_scores = list(umap_acc_list.values())


plt.plot(neighbors, pca_scores, 'b-o', label='PCA', 
         linewidth=2, markersize=8, markerfacecolor='white')
plt.plot(neighbors, umap_scores, 'r-s', label='UMAP', 
         linewidth=2, markersize=8, markerfacecolor='white')

# Add annotations for max values
max_pca = max(pca_scores)
max_umap = max(umap_scores)
plt.annotate(f'Max PCA: {max_pca:.3f}',
             xy=(neighbors[pca_scores.index(max_pca)], max_pca),
             xytext=(10, 10), textcoords='offset points',
             bbox=dict(boxstyle='round,pad=0.5', fc='blue', alpha=0.2))
plt.annotate(f'Max UMAP: {max_umap:.3f}',
             xy=(neighbors[umap_scores.index(max_umap)], max_umap),
             xytext=(10, -20), textcoords='offset points',
             bbox=dict(boxstyle='round,pad=0.5', fc='red', alpha=0.2))


plt.title('KNN Accuracy Comparison: PCA vs UMAP', fontsize=14, pad=20)
plt.xlabel('Number of Neighbors (k)', fontsize=12)
plt.ylabel('Accuracy Score', fontsize=12)
plt.xticks(neighbors)
plt.grid(True, linestyle=':', alpha=0.7)
plt.legend(fontsize=12, framealpha=1)


plt.tight_layout()
plt.show()

PCA has better performance for all n_neighbours in KNN compared to UMAP. We will use PCA for DR

from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

pca = PCA().fit(X)

# Plotting the cumulative explained variance ratio
plt.rcParams["figure.figsize"] = (12,6)

fig, ax = plt.subplots()
xi = np.arange(1, len(X.columns) + 1, step=1)  # The number of components
y = np.cumsum(pca.explained_variance_ratio_)

plt.ylim(0.0, 1.1)
plt.plot(xi, y, marker='o', linestyle='--', color='b')

# Add labels and title
plt.xlabel('Number of Components')
plt.xticks(np.arange(0, len(X.columns), step=1)) 
plt.ylabel('Cumulative variance (%)')
plt.title('The number of components needed to explain variance')

# Add a horizontal line at 95% variance cut-off threshold
plt.axhline(y=0.95, color='r', linestyle='-')
plt.text(0.5, 0.85, '95% cut-off threshold', color = 'red', fontsize=16)

# Show grid on the x-axis
ax.grid(axis='x')

# Display the plot
plt.show()

As we can see, 21 out of the 32 components explain 95% of variance. Therefore, we'll set n_components=0.95. PCA will choose the minimum number of components that explain at least 95% of the total variance

pca = PCA(n_components=0.95)
X_train_reduced = pca.fit_transform(X_resampled)

# Apply the same PCA transformation on the test data
X_test_reduced = pca.transform(X_test)

Baseline Model: Vanilla KNN

from sklearn.metrics import accuracy_score, classification_report
from sklearn.neighbors import KNeighborsClassifier


def knn(X_train, y_train, X_test, y_test, n_neighbors):
    knn = KNeighborsClassifier(n_neighbors)
    knn.fit(X_train, y_train)
    y_pred = knn.predict(X_test)

    print("Baseline Model— Vanilla KNN")
    print(classification_report(y_test, y_pred))
    return y_pred

_ = knn(X_train_reduced, y_resampled, X_test_reduced, y_test, n_neighbors=5)

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

         0.0       0.78      0.68      0.72       503
         1.0       0.63      0.73      0.68       369

    accuracy                           0.70       872
   macro avg       0.70      0.71      0.70       872
weighted avg       0.71      0.70      0.70       872

KMeans Clustering Visualisation

import seaborn as sns
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

def visualize_kmeans_clusters_pca_reduced(X_pca_highdim, y, n_clusters_per_class, random_state=42):
    """
    Visualizes KMeans clustering on PCA-reduced data with red/blue subclass coloring.
    Class 0 = Red Win (shades of red), Class 1 = Blue Win (shades of blue).
    """
    subclass_labels = []
    all_centroids = []
    unique_classes = np.unique(y)

    for label in unique_classes:
        X_class = X_pca_highdim[y == label]
        kmeans = KMeans(n_clusters=n_clusters_per_class, random_state=random_state)
        cluster_ids = kmeans.fit_predict(X_class)

        # Decimal subclass label: e.g., 0.0, 0.1, 1.0, 1.1
        subclass_labels.extend([label + (cid / 10) for cid in cluster_ids])
        all_centroids.append(kmeans.cluster_centers_)

    subclass_labels = np.array(subclass_labels)
    all_centroids = np.vstack(all_centroids)

    # Project to 2D PCA for plotting
    pca2 = PCA(n_components=2, random_state=0)
    X2 = pca2.fit_transform(X_pca_highdim)
    C2 = pca2.transform(all_centroids)

    # Format labels for consistent coloring
    subclass_labels_str = [f"{s:.1f}" for s in subclass_labels]
    unique_subclasses = sorted(np.unique(subclass_labels_str))

    # Assign red shades for class 0, blue for class 1
    red_subs = [lbl for lbl in unique_subclasses if lbl.startswith("0")]
    blue_subs = [lbl for lbl in unique_subclasses if lbl.startswith("1")]

    red_palette = (
        ["#c62828"] if len(red_subs) == 1 else sns.color_palette("Reds", n_colors=len(red_subs))
    )
    blue_palette = (
        ["#1565c0"] if len(blue_subs) == 1 else sns.color_palette("Blues", n_colors=len(blue_subs))
    )

    color_map = dict(zip(red_subs, red_palette))
    color_map.update(dict(zip(blue_subs, blue_palette)))
    point_colors = [color_map[label] for label in subclass_labels_str]

    # Plot
    plt.figure(figsize=(10, 6))
    plt.scatter(X2[:, 0], X2[:, 1], c=point_colors, alpha=0.7,
                s=50, edgecolor="k", linewidth=0.5)

    # Centroids
    plt.scatter(C2[:, 0], C2[:, 1], marker="X", s=140, c="black",
                edgecolor="white", linewidth=1.5, label="Centroids")

    # Axis labels
    plt.xlabel("PCA Component 1")
    plt.ylabel("PCA Component 2")
    plt.title("KMeans Clustering with Subclass Colors (Red = Class 0, Blue = Class 1)")

    # Legend
    handles = [plt.Line2D([0], [0], marker='o', color='w', label=lbl,
                          markerfacecolor=color_map[lbl], markersize=8, markeredgecolor="k")
               for lbl in unique_subclasses]
    plt.legend(handles=handles, title="Subclass", bbox_to_anchor=(1.05, 1), loc="upper left")

    plt.grid(True)
    plt.tight_layout()
    plt.show()

# Get the PCA components (loadings matrix)
loadings = pca.components_  # Shape: (n_components=21, n_original_features=32)
feature_names = X_train.columns.tolist()  # Original feature names

# Top features for PC1 and PC2 (first two components)
top_n = 3  # Number of top features to extract
top_pc1_idx = np.argsort(np.abs(loadings[0]))[-top_n:]  # Indices for PC1
top_pc2_idx = np.argsort(np.abs(loadings[1]))[-top_n:]   # Indices for PC2

top_pc1_features = [feature_names[i] for i in top_pc1_idx]
top_pc2_features = [feature_names[i] for i in top_pc2_idx]

print(f"Top {top_n} features for PC1:", top_pc1_features)
print(f"Top {top_n} features for PC2:", top_pc2_features)

Top 3 features for PC1: ['BlueFighter_Skill', 'RedOdds', 'BlueOdds']
Top 3 features for PC2: ['BlueAvgSigStrLanded', 'MonthsAgo', 'RecencyWeight']

visualize_kmeans_clusters_pca_reduced(X_train_reduced, y_resampled, n_clusters_per_class=2)

Algorithm 1: Weighted Dynamic Clustering with Adaptive Euclidean Distance (w/ adaptive weights and dynamic cluster reassignment)

def weighted_dynamic_clustering_custom(X, y, n_clusters_per_class, max_iter=100, tol=1e-6):
    """
    Returns:
    - centroids: Subclass centroids.
    - centroid_labels: Parent class labels for centroids (e.g., [0, 0, 1, 1]).
    - subclass_labels: Subclass assignments in decimal format (e.g., 0.0, 0.1, 1.0).
    - weights: Feature weights per subclass.
    """
    unique_labels = np.unique(y)
    centroids = []
    centroid_labels = []
    subclass_labels = np.zeros(len(X))  # Stores subclass assignments
    weights = []

    for label in unique_labels:
        X_class = X[y == label]
        n_clusters = n_clusters_per_class if isinstance(n_clusters_per_class, int) else n_clusters_per_class[label]

        # Random initialization of centroids and weights
        centroid_indices = np.random.choice(len(X_class), n_clusters, replace=False)
        centroids_class = X_class[centroid_indices]
        weights_class = np.ones((n_clusters, X.shape[1])) / X.shape[1]  # Uniform initial weights

        prev_partition = np.zeros(len(X_class))

        for _ in range(max_iter):
            # Assign points to subclasses using weighted Euclidean distance
            distances = np.zeros((len(X_class), n_clusters))
            for k in range(n_clusters):
                diff = X_class - centroids_class[k]
                weighted_diff = diff ** 2 * weights_class[k]
                distances[:, k] = np.sum(weighted_diff, axis=1)
            
            cluster_ids = np.argmin(distances, axis=1)

            # Check convergence
            if np.all(cluster_ids == prev_partition):
                break
            prev_partition = cluster_ids.copy()

            # Update centroids and weights
            for k in range(n_clusters):
                X_cluster = X_class[cluster_ids == k]
                if len(X_cluster) == 0:
                    continue
                
                # Update centroid (Eq. 12)
                centroids_class[k] = np.mean(X_cluster, axis=0)
                
                # Update weights (Eq. 19)
                var = np.var(X_cluster, axis=0) + 1e-8  # Avoid division by zero
                weights_class[k] = (1 / var) / np.sum(1 / var)

        # Store results for this class
        centroids.extend(centroids_class)
        centroid_labels.extend([label] * n_clusters)
        
        # Assigns decimal-format subclass labels 

        subclass_labels[y == label] = label + (cluster_ids / 10)  # e.g., 0.0, 0.1, 1.0, 1.1
        
        weights.extend(weights_class)

    return np.array(centroids), np.array(centroid_labels), subclass_labels, np.array(weights)

DC 2D Visualisation

def visualize_clusters_custom(X_pca_highdim, centroids_highdim, subclass_labels,
                                 top_pc1_feats=None, top_pc2_feats=None):
    """
    Visualize DC-KNN subclasses in 2D PCA, using strong red/blue for class 0 and 1.
    """
    # Project down to 2D
    pca2 = PCA(n_components=2, random_state=0)
    X2 = pca2.fit_transform(X_pca_highdim)
    C2 = pca2.transform(centroids_highdim)

    # Format labels
    subclass_labels_str = [f"{s:.1f}" for s in subclass_labels]
    unique_labels = sorted(np.unique(subclass_labels_str))

    # Split into red and blue class subclasses
    red_subs = [lbl for lbl in unique_labels if lbl.startswith("0")]
    blue_subs = [lbl for lbl in unique_labels if lbl.startswith("1")]

    # Use stronger red/blue palettes — manually pick if only 1 subclass
    red_palette = (
        ["#c62828"] if len(red_subs) == 1 else sns.color_palette("Reds", n_colors=len(red_subs))
    )
    blue_palette = (
        ["#1565c0"] if len(blue_subs) == 1 else sns.color_palette("Blues", n_colors=len(blue_subs))
    )

    color_map = dict(zip(red_subs, red_palette))
    color_map.update(dict(zip(blue_subs, blue_palette)))

    # Assign point colors
    point_colors = [color_map[label] for label in subclass_labels_str]

    # Plot
    plt.figure(figsize=(12, 8))
    plt.scatter(X2[:, 0], X2[:, 1], c=point_colors, s=60, alpha=0.75,
                edgecolor="black", linewidth=0.6)

    # Plot centroids
    plt.scatter(C2[:, 0], C2[:, 1], c="black", marker="X", s=200,
                edgecolor="white", linewidth=1.5, label="Centroids")

    # Labels
    xlabel = "PC1"
    ylabel = "PC2"
    if top_pc1_feats:
        xlabel += f" (Top: {top_pc1_feats})"
    if top_pc2_feats:
        ylabel += f" (Top: {top_pc2_feats})"
    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    plt.title("Weighted Dynamic Clustering (Red = Class 0, Blue = Class 1)")

    # Custom legend
    handles = [plt.Line2D([0], [0], marker='o', color='w', label=lbl,
                          markerfacecolor=color_map[lbl], markersize=8, markeredgecolor="k")
               for lbl in unique_labels]
    plt.legend(handles=handles, title="Subclass", bbox_to_anchor=(1.05, 1), loc="upper left")

    plt.grid(True)
    plt.tight_layout()
    plt.show()

centroids, labels, subclass_labels, weights = weighted_dynamic_clustering_custom(
    X_train_reduced, y_resampled,
    n_clusters_per_class=2,
)
visualize_clusters_custom(
    X_train_reduced,
    centroids,
    subclass_labels,
    top_pc1_feats=top_pc1_features,
    top_pc2_feats=top_pc2_features
)

Algorithm 2.1: DC-KNN Combining Algorithm (w/ weighted KNN)

from sklearn.neighbors import NearestNeighbors
from collections import Counter

# Weighted distance function (Eq.22)
def weighted_euclidean(u, v, w):
    return np.sqrt(np.sum(w * (u - v) ** 2))
    
def dc_knn_combining_algorithm(X_train, y_train, X_test, y_test, n_clusters_per_class, k, distance_metric):
    """Implements Algorithm 2 from the paper: DC-KNN with clustering and weighted KNN on original samples."""
    # Dynamic clustering
    centroids, centroid_labels, subclass_labels, weights = weighted_dynamic_clustering_custom(
        X_train, y_train, n_clusters_per_class)
    
    # Map original training sample index to subclass index
    unique_subclass_labels = np.unique(subclass_labels)
    subclass_index_mapping = {label: idx for idx, label in enumerate(unique_subclass_labels)}
    sample_to_subclass = np.array([subclass_index_mapping[label] for label in subclass_labels])

    # Prepare KNN on original X_train
    neigh = NearestNeighbors(n_neighbors=k, algorithm='auto')
    neigh.fit(X_train)

    y_pred = []
    for x in X_test:
        # Find k nearest neighbors (unweighted for initial selection)
        distances, indices = neigh.kneighbors([x])
        indices = indices[0]

        # For each neighbor, calculate weighted distance using its subclass weights
        weighted_dists = []
        for idx in indices:
            subclass_id = sample_to_subclass[idx]
            subclass_weight = weights[subclass_id]
            
            # Use passed-in distance function
            if distance_metric.__code__.co_argcount == 2:
                dist = distance_metric(x, X_train[idx])
            else:
                dist = distance_metric(x, X_train[idx], subclass_weight)

            weighted_dists.append((dist, subclass_id, y_train.iloc[idx]))
  

            
        # Sort neighbors by weighted distance
        weighted_dists.sort(key=lambda tup: tup[0])

        # Get most frequent subclass among top-k
        top_k_subclass_ids = [tup[1] for tup in weighted_dists[:k]]
        most_common_subclass = Counter(top_k_subclass_ids).most_common(1)[0][0]

        # Convert back to class label
        pred_class = int(str(unique_subclass_labels[most_common_subclass])[0])  # e.g. from 1.2 → 1
        y_pred.append(pred_class)

    print("Algorithm 2 — DC-KNN with Standard Data-Level KNN")
    print(classification_report(y_test, y_pred))
    return np.array(y_pred)

Algorithm 3: DC-KNN Using Centroids as Nearest Neighbors

def dc_knn_centroid_nn(X_train, y_train, X_test, y_test, n_clusters_per_class,k, distance_metric):
    """Implements Algorithm 3: DC-KNN using Centroids as Nearest Neighbors. """
    # Perform Dynamic Clustering (Algorithm 1)
    centroids, centroid_labels, _, weights = weighted_dynamic_clustering_custom(
        X_train, y_train, n_clusters_per_class
    )

    # Predict class based on closest centroids
    y_pred = []
    for x in X_test:
        # Compute distance from x to each centroid
        distances = np.array([
            distance_metric(x, c) if distance_metric.__code__.co_argcount == 2 
            else distance_metric(x, c, w)
            for c, w in zip(centroids, weights)
        ])

        # Get k nearest centroid indices
        nearest_idxs = np.argsort(distances)[:k]
        nearest_labels = [centroid_labels[i] for i in nearest_idxs]

        # Majority vote
        pred = np.bincount(nearest_labels).argmax()
        y_pred.append(pred)

    # Evaluation
    print("Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors")
    print(classification_report(y_test, y_pred))

    return np.array(y_pred)

Baseline Evaluation

Standard DC-KNN

_ = dc_knn_combining_algorithm(X_train_reduced, y_resampled, X_test_reduced, y_test, n_clusters_per_class=2, k=5, distance_metric=weighted_euclidean)

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

         0.0       0.78      0.66      0.72       503
         1.0       0.62      0.75      0.68       369

    accuracy                           0.70       872
   macro avg       0.70      0.70      0.70       872
weighted avg       0.71      0.70      0.70       872

Test C (DC-KNN - Centroid-base nearest neighbour)

_ = dc_knn_centroid_nn(
    X_train_reduced, 
    y_resampled,
    X_test_reduced,
    y_test,
    n_clusters_per_class=3,
    k=5,
    distance_metric=weighted_euclidean
)

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

         0.0       0.74      0.79      0.76       503
         1.0       0.68      0.62      0.65       369

    accuracy                           0.72       872
   macro avg       0.71      0.70      0.71       872
weighted avg       0.71      0.72      0.71       872

Hyper-Parameter Tuning

from sklearn.model_selection import StratifiedKFold
from itertools import product
from scipy.spatial.distance import euclidean, cityblock  # cityblock = Manhattan

param_grid = {
    "n_clusters_per_class": [2, 3, 4, 5, 6, 7, 8, 9, 10],
    "k_neighbours": [3, 5, 7, 9, 11, 13, 15, 17, 19],
    "distance_metric": [euclidean, cityblock, weighted_euclidean]
}

def hyperparameter_tune(model, X_train, y_train, param_grid):
    best_score = 0
    best_combo = None
    all_combos = {}

    # Choose grid depending on model
    if model == knn:
        grid = product(param_grid['k_neighbours'])
    else:
        grid = product(
            param_grid['n_clusters_per_class'],
            param_grid['k_neighbours'],
            param_grid['distance_metric']
        )

    # 5-fold stratified cross-validation
    cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

    for combo in grid:
        acc_scores = []

        for train_idx, test_idx in cv.split(X_train, y_train):
            X_tr, X_te = X_train[train_idx], X_train[test_idx]
            y_tr, y_te = y_train.iloc[train_idx], y_train.iloc[test_idx]

            if model == knn:
                k = combo[0]
                y_pred = model(X_tr, y_tr, X_te, y_te, n_neighbors=k)
                log_str = f"k_neighbours={k}"

            else:
                n_clusters, k, metric = combo

                # Special constraint for centroid-level DC-KNN
                if model.__name__ == "dc_knn_centroid_nn" and k >= 2 * n_clusters:
                    continue

                y_pred = model(
                    X_tr, y_tr, X_te, y_te,
                    n_clusters_per_class=n_clusters,
                    k=k,
                    distance_metric=metric
                )
                log_str = f"n_clusters={n_clusters}, k_neighbours={k}, metric={metric}"

            # Append after prediction
            acc_scores.append(accuracy_score(y_te, y_pred))

        if acc_scores:  # Only computes mean if not empty
            mean_acc = np.mean(acc_scores)
            all_combos[combo] = mean_acc
            print(f"Tested: {log_str} --> Acc: {mean_acc:.4f}")

            if mean_acc > best_score:
                best_score = mean_acc
                best_combo = combo

    print(f"\nBest Params for {model.__name__}: {best_combo} → Accuracy: {round(best_score, 4)}")
    return best_combo, all_combos

best_params_dc_knn, all_combos_dc_knn = hyperparameter_tune(
    model=dc_knn_combining_algorithm,  
    X_train=X_train_reduced,
    y_train=y_resampled,
    param_grid=param_grid
)

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.68      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.68      0.72       402
           1       0.71      0.78      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.67      0.74       402
           1       0.72      0.85      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.60      0.68       402
           1       0.67      0.83      0.74       402

    accuracy                           0.71       804
   macro avg       0.73      0.71      0.71       804
weighted avg       0.73      0.71      0.71       804

Tested: n_clusters=2, k_neighbours=3, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7371
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.68      0.73       402
           1       0.72      0.82      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.67      0.71       402
           1       0.70      0.78      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.65      0.73       402
           1       0.71      0.85      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.62      0.70       402
           1       0.69      0.83      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.72       804
weighted avg       0.74      0.73      0.72       804

Tested: n_clusters=2, k_neighbours=3, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7396
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.69      0.74       402
           1       0.73      0.82      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.67      0.71       402
           1       0.71      0.79      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.67      0.73       402
           1       0.72      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.63      0.70       402
           1       0.69      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Tested: n_clusters=2, k_neighbours=3, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7413
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.65      0.71       402
           1       0.70      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.67      0.71       402
           1       0.71      0.78      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.68      0.74       402
           1       0.73      0.84      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.63      0.69       402
           1       0.68      0.80      0.74       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Tested: n_clusters=2, k_neighbours=5, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7361
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.67      0.72       402
           1       0.71      0.80      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.66      0.71       402
           1       0.70      0.79      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.65      0.72       402
           1       0.71      0.86      0.78       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.62      0.69       402
           1       0.68      0.83      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=2, k_neighbours=5, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7348
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.68      0.73       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.67      0.73       402
           1       0.72      0.82      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.64      0.69       402
           1       0.69      0.79      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.70      0.76       402
           1       0.74      0.84      0.79       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.63      0.69       402
           1       0.69      0.81      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=2, k_neighbours=5, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7408
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.67      0.71       402
           1       0.71      0.80      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.70      0.73       402
           1       0.72      0.79      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.68      0.72       402
           1       0.71      0.80      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.68      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.67      0.71       402
           1       0.70      0.79      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Tested: n_clusters=2, k_neighbours=7, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7371
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.67      0.73       402
           1       0.71      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.69      0.73       402
           1       0.72      0.79      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.70      0.74       402
           1       0.73      0.79      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.68      0.73       402
           1       0.72      0.80      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.64      0.70       402
           1       0.69      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Tested: n_clusters=2, k_neighbours=7, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7405
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.65      0.70       402
           1       0.70      0.80      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.68      0.72       402
           1       0.71      0.79      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.67      0.73       402
           1       0.72      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.63      0.69       402
           1       0.69      0.80      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=2, k_neighbours=7, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7326
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.65      0.71       402
           1       0.70      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.69      0.73       402
           1       0.72      0.79      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.70      0.73       402
           1       0.72      0.78      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.62      0.69       402
           1       0.68      0.81      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.71       804
weighted avg       0.72      0.72      0.71       804

Tested: n_clusters=2, k_neighbours=9, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7351
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.65      0.72       402
           1       0.71      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.68      0.73       402
           1       0.72      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.72      0.74       402
           1       0.73      0.76      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.63      0.69       402
           1       0.69      0.81      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=2, k_neighbours=9, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7393
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.67      0.71       402
           1       0.70      0.80      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.70      0.73       402
           1       0.72      0.79      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.69      0.72       402
           1       0.72      0.78      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.68      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.63      0.68       402
           1       0.68      0.79      0.73       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Tested: n_clusters=2, k_neighbours=9, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7338
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.70      0.73       402
           1       0.72      0.80      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.71      0.76       402
           1       0.74      0.83      0.79       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.70      0.73       402
           1       0.72      0.79      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.70      0.74       402
           1       0.73      0.80      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.61      0.68       402
           1       0.68      0.81      0.74       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Tested: n_clusters=2, k_neighbours=11, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7453
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.62      0.69       402
           1       0.69      0.84      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.72       804
weighted avg       0.74      0.73      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.75      0.78       402
           1       0.77      0.82      0.79       402

    accuracy                           0.78       804
   macro avg       0.78      0.78      0.78       804
weighted avg       0.78      0.78      0.78       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.69      0.73       402
           1       0.72      0.79      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.66      0.73       402
           1       0.71      0.85      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.67      0.72       402
           1       0.71      0.80      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Tested: n_clusters=2, k_neighbours=11, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7480
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.68      0.72       402
           1       0.71      0.79      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.71      0.76       402
           1       0.74      0.83      0.78       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.65      0.72       402
           1       0.71      0.85      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.72      0.75       402
           1       0.74      0.81      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.64      0.69       402
           1       0.69      0.80      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Tested: n_clusters=2, k_neighbours=11, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7480
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.70      0.75       402
           1       0.73      0.82      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.83      0.67      0.74       402
           1       0.72      0.86      0.79       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.73      0.75       402
           1       0.74      0.77      0.75       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.72      0.75       402
           1       0.74      0.81      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.63      0.70       402
           1       0.69      0.84      0.76       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=2, k_neighbours=13, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7550
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.65      0.72       402
           1       0.71      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.66      0.73       402
           1       0.72      0.86      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.69      0.73       402
           1       0.72      0.79      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.69      0.74       402
           1       0.73      0.83      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.66      0.71       402
           1       0.70      0.80      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Tested: n_clusters=2, k_neighbours=13, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7458
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.64      0.72       402
           1       0.70      0.86      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.74       804
weighted avg       0.76      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.75      0.77       402
           1       0.76      0.79      0.78       402

    accuracy                           0.77       804
   macro avg       0.77      0.77      0.77       804
weighted avg       0.77      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.68      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.61      0.69       402
           1       0.68      0.82      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.71       804
weighted avg       0.73      0.72      0.71       804

Tested: n_clusters=2, k_neighbours=13, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7438
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.70      0.75       402
           1       0.73      0.82      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.67      0.74       402
           1       0.72      0.86      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.68      0.72       402
           1       0.71      0.80      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.68      0.74       402
           1       0.72      0.83      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.59      0.68       402
           1       0.68      0.85      0.75       402

    accuracy                           0.72       804
   macro avg       0.74      0.72      0.72       804
weighted avg       0.74      0.72      0.72       804

Tested: n_clusters=2, k_neighbours=15, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7480
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.70      0.75       402
           1       0.74      0.83      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.72      0.74       402
           1       0.74      0.79      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.68      0.72       402
           1       0.71      0.78      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.69      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.63      0.70       402
           1       0.69      0.82      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=2, k_neighbours=15, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7435
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.70      0.75       402
           1       0.73      0.83      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.71      0.75       402
           1       0.74      0.82      0.78       402

    accuracy                           0.77       804
   macro avg       0.77      0.77      0.77       804
weighted avg       0.77      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.63      0.70       402
           1       0.69      0.84      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.73       804
weighted avg       0.75      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.62      0.70       402
           1       0.69      0.85      0.76       402

    accuracy                           0.73       804
   macro avg       0.75      0.73      0.73       804
weighted avg       0.75      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.62      0.71       402
           1       0.69      0.85      0.77       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Tested: n_clusters=2, k_neighbours=15, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7475
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.70      0.75       402
           1       0.74      0.84      0.78       402

    accuracy                           0.77       804
   macro avg       0.77      0.77      0.77       804
weighted avg       0.77      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.70      0.75       402
           1       0.74      0.84      0.79       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.66      0.71       402
           1       0.70      0.78      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.65      0.73       402
           1       0.71      0.86      0.78       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.59      0.67       402
           1       0.67      0.83      0.74       402

    accuracy                           0.71       804
   macro avg       0.73      0.71      0.71       804
weighted avg       0.73      0.71      0.71       804

Tested: n_clusters=2, k_neighbours=17, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7463
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.69      0.74       402
           1       0.72      0.82      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.74      0.77       402
           1       0.76      0.81      0.78       402

    accuracy                           0.77       804
   macro avg       0.77      0.77      0.77       804
weighted avg       0.77      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.74      0.69      0.72       402
           1       0.71      0.76      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.65      0.73       402
           1       0.71      0.85      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Tested: n_clusters=2, k_neighbours=17, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7493
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.65      0.72       402
           1       0.71      0.85      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.74      0.77       402
           1       0.76      0.81      0.79       402

    accuracy                           0.78       804
   macro avg       0.78      0.78      0.78       804
weighted avg       0.78      0.78      0.78       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.69      0.72       402
           1       0.71      0.77      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.68      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.80      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=2, k_neighbours=17, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7463
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.71      0.75       402
           1       0.74      0.83      0.78       402

    accuracy                           0.77       804
   macro avg       0.77      0.77      0.77       804
weighted avg       0.77      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.70      0.74       402
           1       0.73      0.81      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.62      0.70       402
           1       0.69      0.83      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=2, k_neighbours=19, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7445
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.69      0.74       402
           1       0.72      0.82      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.70      0.75       402
           1       0.73      0.83      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.67      0.72       402
           1       0.71      0.80      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.66      0.72       402
           1       0.71      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.59      0.68       402
           1       0.67      0.85      0.75       402

    accuracy                           0.72       804
   macro avg       0.74      0.72      0.72       804
weighted avg       0.74      0.72      0.72       804

Tested: n_clusters=2, k_neighbours=19, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7445
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.63      0.71       402
           1       0.70      0.86      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.74       804
weighted avg       0.76      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.72      0.75       402
           1       0.74      0.81      0.77       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.69      0.72       402
           1       0.71      0.77      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.67      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.62      0.70       402
           1       0.69      0.85      0.76       402

    accuracy                           0.73       804
   macro avg       0.75      0.73      0.73       804
weighted avg       0.75      0.73      0.73       804

Tested: n_clusters=2, k_neighbours=19, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7433
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.63      0.69       402
           1       0.69      0.81      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.65      0.70       402
           1       0.69      0.80      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.67      0.73       402
           1       0.71      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.61      0.69       402
           1       0.68      0.83      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=3, k_neighbours=3, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7311
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.67      0.73       402
           1       0.71      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.64      0.70       402
           1       0.69      0.79      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.66      0.73       402
           1       0.72      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.63      0.69       402
           1       0.69      0.81      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=3, k_neighbours=3, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7363
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.64      0.71       402
           1       0.70      0.83      0.76       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.68      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.66      0.71       402
           1       0.70      0.79      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.65      0.72       402
           1       0.70      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.62      0.69       402
           1       0.69      0.82      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=3, k_neighbours=3, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7333
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.65      0.71       402
           1       0.70      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.66      0.70       402
           1       0.70      0.78      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.68      0.74       402
           1       0.73      0.84      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.62      0.68       402
           1       0.68      0.81      0.74       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Tested: n_clusters=3, k_neighbours=5, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7343
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.63      0.70       402
           1       0.69      0.83      0.76       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.67      0.72       402
           1       0.71      0.80      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.64      0.69       402
           1       0.68      0.78      0.73       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.66      0.73       402
           1       0.72      0.86      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.61      0.67       402
           1       0.67      0.80      0.73       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.70       804
weighted avg       0.71      0.71      0.70       804

Tested: n_clusters=3, k_neighbours=5, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7284
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.65      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.64      0.71       402
           1       0.70      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.66      0.73       402
           1       0.72      0.86      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.62      0.68       402
           1       0.68      0.81      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.71       804
weighted avg       0.72      0.72      0.71       804

Tested: n_clusters=3, k_neighbours=5, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7346
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.65      0.72       402
           1       0.70      0.84      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.65      0.70       402
           1       0.69      0.79      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Tested: n_clusters=3, k_neighbours=7, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7356
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.80      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.68      0.73       402
           1       0.72      0.82      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.67      0.71       402
           1       0.71      0.79      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.67      0.73       402
           1       0.72      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.63      0.69       402
           1       0.69      0.82      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=3, k_neighbours=7, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7373
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.67      0.73       402
           1       0.72      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.65      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.68      0.73       402
           1       0.72      0.82      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.63      0.70       402
           1       0.69      0.83      0.76       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=3, k_neighbours=7, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7408
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.68      0.72       402
           1       0.71      0.79      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.69      0.74       402
           1       0.73      0.84      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.67      0.71       402
           1       0.70      0.79      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.67      0.73       402
           1       0.71      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.62      0.69       402
           1       0.69      0.83      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.72       804
weighted avg       0.74      0.73      0.72       804

Tested: n_clusters=3, k_neighbours=9, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7393
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.61      0.68       402
           1       0.68      0.83      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.71       804
weighted avg       0.73      0.72      0.71       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.67      0.73       402
           1       0.71      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.69      0.72       402
           1       0.72      0.78      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.64      0.71       402
           1       0.70      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.65      0.70       402
           1       0.69      0.78      0.73       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Tested: n_clusters=3, k_neighbours=9, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7313
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.62      0.69       402
           1       0.68      0.83      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.67      0.73       402
           1       0.71      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.70      0.73       402
           1       0.72      0.79      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.65      0.71       402
           1       0.70      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.64      0.70       402
           1       0.69      0.80      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=3, k_neighbours=9, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7336
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.69      0.75       402
           1       0.73      0.85      0.78       402

    accuracy                           0.77       804
   macro avg       0.77      0.77      0.76       804
weighted avg       0.77      0.77      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.67      0.72       402
           1       0.71      0.80      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.84      0.67      0.74       402
           1       0.72      0.87      0.79       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.62      0.69       402
           1       0.68      0.82      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.71       804
weighted avg       0.73      0.72      0.71       804

Tested: n_clusters=3, k_neighbours=11, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7460
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.64      0.70       402
           1       0.69      0.81      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.71      0.75       402
           1       0.74      0.81      0.77       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.64      0.71       402
           1       0.70      0.84      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.65      0.72       402
           1       0.71      0.85      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.61      0.69       402
           1       0.68      0.83      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=3, k_neighbours=11, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7393
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.71      0.75       402
           1       0.74      0.81      0.77       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.70      0.74       402
           1       0.73      0.80      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.68      0.73       402
           1       0.72      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Tested: n_clusters=3, k_neighbours=11, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7480
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.69      0.74       402
           1       0.73      0.83      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.71      0.73       402
           1       0.72      0.76      0.74       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.67      0.74       402
           1       0.72      0.85      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.65      0.70       402
           1       0.69      0.79      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Tested: n_clusters=3, k_neighbours=13, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7435
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.68      0.73       402
           1       0.72      0.82      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.68      0.74       402
           1       0.72      0.84      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.66      0.71       402
           1       0.70      0.79      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.63      0.70       402
           1       0.69      0.84      0.76       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.61      0.68       402
           1       0.68      0.83      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=3, k_neighbours=13, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7376
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.65      0.72       402
           1       0.71      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.74       804
weighted avg       0.76      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.71      0.76       402
           1       0.74      0.83      0.78       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.69      0.73       402
           1       0.72      0.79      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.67      0.74       402
           1       0.72      0.85      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.62      0.69       402
           1       0.68      0.82      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=3, k_neighbours=13, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7480
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.69      0.74       402
           1       0.73      0.83      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.68      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.68      0.71       402
           1       0.71      0.77      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.64      0.71       402
           1       0.70      0.85      0.77       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.62      0.69       402
           1       0.68      0.82      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=3, k_neighbours=15, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7391
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.68      0.72       402
           1       0.71      0.80      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.69      0.73       402
           1       0.72      0.79      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.69      0.73       402
           1       0.72      0.80      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.65      0.71       402
           1       0.70      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=3, k_neighbours=15, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7393
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.69      0.74       402
           1       0.73      0.83      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.68      0.74       402
           1       0.73      0.83      0.78       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.74      0.70      0.72       402
           1       0.72      0.76      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.71      0.74       402
           1       0.73      0.80      0.76       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.64      0.70       402
           1       0.70      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=3, k_neighbours=15, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7463
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.73      0.76       402
           1       0.75      0.80      0.77       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.71      0.76       402
           1       0.74      0.83      0.78       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.63      0.70       402
           1       0.69      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.64      0.72       402
           1       0.70      0.85      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.74       804
weighted avg       0.76      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.62      0.70       402
           1       0.69      0.84      0.76       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=3, k_neighbours=17, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7478
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.83      0.70      0.76       402
           1       0.74      0.86      0.80       402

    accuracy                           0.78       804
   macro avg       0.79      0.78      0.78       804
weighted avg       0.79      0.78      0.78       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.68      0.71       402
           1       0.71      0.78      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.62      0.70       402
           1       0.69      0.86      0.77       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.64      0.70       402
           1       0.69      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=3, k_neighbours=17, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7433
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.65      0.73       402
           1       0.71      0.85      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.68      0.74       402
           1       0.72      0.84      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.67      0.73       402
           1       0.71      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.64      0.70       402
           1       0.69      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Tested: n_clusters=3, k_neighbours=17, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7448
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.65      0.72       402
           1       0.71      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.68      0.73       402
           1       0.72      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.68      0.72       402
           1       0.71      0.80      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.63      0.69       402
           1       0.68      0.81      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.60      0.68       402
           1       0.68      0.83      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.71       804
weighted avg       0.73      0.72      0.71       804

Tested: n_clusters=3, k_neighbours=19, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7358
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.65      0.72       402
           1       0.70      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.69      0.72       402
           1       0.72      0.78      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.66      0.72       402
           1       0.71      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.65      0.70       402
           1       0.70      0.80      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Tested: n_clusters=3, k_neighbours=19, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7396
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.83      0.67      0.74       402
           1       0.73      0.86      0.79       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.62      0.70       402
           1       0.69      0.85      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.73       804
weighted avg       0.75      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.67      0.71       402
           1       0.71      0.78      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Tested: n_clusters=3, k_neighbours=19, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7428
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.64      0.71       402
           1       0.70      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.67      0.71       402
           1       0.70      0.79      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.66      0.72       402
           1       0.71      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.62      0.70       402
           1       0.69      0.83      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.72       804
weighted avg       0.74      0.73      0.72       804

Tested: n_clusters=4, k_neighbours=3, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7353
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.71       402
           1       0.70      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.68      0.73       402
           1       0.72      0.82      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.67      0.71       402
           1       0.70      0.79      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.68      0.74       402
           1       0.72      0.85      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.63      0.71       402
           1       0.70      0.85      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.73       804
weighted avg       0.75      0.74      0.73       804

Tested: n_clusters=4, k_neighbours=3, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7430
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.67      0.73       402
           1       0.72      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.66      0.71       402
           1       0.70      0.80      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.67      0.72       402
           1       0.71      0.83      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.63      0.71       402
           1       0.70      0.84      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.73       804
weighted avg       0.75      0.74      0.73       804

Tested: n_clusters=4, k_neighbours=3, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7403
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.71       402
           1       0.70      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.66      0.70       402
           1       0.69      0.78      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.68      0.74       402
           1       0.73      0.86      0.79       402

    accuracy                           0.77       804
   macro avg       0.77      0.77      0.76       804
weighted avg       0.77      0.77      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.61      0.68       402
           1       0.67      0.80      0.73       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Tested: n_clusters=4, k_neighbours=5, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7333
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.68      0.71       402
           1       0.71      0.78      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.66      0.73       402
           1       0.71      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.62      0.68       402
           1       0.68      0.80      0.73       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Tested: n_clusters=4, k_neighbours=5, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7338
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.65      0.70       402
           1       0.69      0.79      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.65      0.72       402
           1       0.71      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.62      0.68       402
           1       0.68      0.79      0.73       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.70       804
weighted avg       0.71      0.71      0.70       804

Tested: n_clusters=4, k_neighbours=5, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7279
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.66      0.72       402
           1       0.71      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.69      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.62      0.68       402
           1       0.68      0.80      0.74       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Tested: n_clusters=4, k_neighbours=7, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7361
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.65      0.70       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.68      0.73       402
           1       0.72      0.80      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.62      0.68       402
           1       0.68      0.79      0.73       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Tested: n_clusters=4, k_neighbours=7, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7321
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.68      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.68      0.74       402
           1       0.72      0.83      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.75       804
weighted avg       0.76      0.76      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.65      0.70       402
           1       0.70      0.80      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.70      0.75       402
           1       0.74      0.84      0.79       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.63      0.70       402
           1       0.69      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Tested: n_clusters=4, k_neighbours=7, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7448
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.65      0.70       402
           1       0.69      0.79      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.67      0.72       402
           1       0.71      0.80      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.63      0.69       402
           1       0.69      0.81      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.59      0.67       402
           1       0.67      0.83      0.74       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Tested: n_clusters=4, k_neighbours=9, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7269
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.68      0.72       402
           1       0.71      0.79      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.69      0.72       402
           1       0.71      0.78      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.65      0.72       402
           1       0.71      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.63      0.70       402
           1       0.69      0.83      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.72       804
weighted avg       0.74      0.73      0.72       804

Tested: n_clusters=4, k_neighbours=9, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7351
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.65      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.67      0.73       402
           1       0.72      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.68      0.72       402
           1       0.71      0.80      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.65      0.71       402
           1       0.70      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.62      0.70       402
           1       0.69      0.84      0.76       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=4, k_neighbours=9, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7373
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.66      0.72       402
           1       0.71      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.70      0.74       402
           1       0.73      0.82      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.71      0.74       402
           1       0.73      0.79      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.68      0.74       402
           1       0.73      0.85      0.78       402

    accuracy                           0.77       804
   macro avg       0.77      0.77      0.76       804
weighted avg       0.77      0.77      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.64      0.70       402
           1       0.69      0.80      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Tested: n_clusters=4, k_neighbours=11, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7465
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.80      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.70      0.74       402
           1       0.73      0.81      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.69      0.73       402
           1       0.72      0.80      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.68      0.73       402
           1       0.72      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.59      0.67       402
           1       0.67      0.82      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Tested: n_clusters=4, k_neighbours=11, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7376
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.65      0.71       402
           1       0.70      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.71      0.76       402
           1       0.74      0.84      0.79       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.71      0.73       402
           1       0.72      0.77      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.70      0.74       402
           1       0.73      0.81      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.80      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=4, k_neighbours=11, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7455
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.70      0.75       402
           1       0.74      0.84      0.78       402

    accuracy                           0.77       804
   macro avg       0.77      0.77      0.77       804
weighted avg       0.77      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.65      0.71       402
           1       0.70      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.64      0.71       402
           1       0.70      0.85      0.77       402

    accuracy                           0.74       804
   macro avg       0.76      0.74      0.74       804
weighted avg       0.76      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.60      0.68       402
           1       0.68      0.83      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.71       804
weighted avg       0.73      0.72      0.71       804

Tested: n_clusters=4, k_neighbours=13, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7413
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.65      0.71       402
           1       0.70      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.71      0.75       402
           1       0.74      0.81      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.61      0.68       402
           1       0.68      0.81      0.74       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Tested: n_clusters=4, k_neighbours=13, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7376
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.67      0.74       402
           1       0.72      0.85      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.69      0.74       402
           1       0.73      0.82      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.69      0.73       402
           1       0.72      0.79      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.67      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.63      0.70       402
           1       0.69      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Tested: n_clusters=4, k_neighbours=13, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7465
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.67      0.74       402
           1       0.72      0.85      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.70      0.74       402
           1       0.73      0.83      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.71      0.73       402
           1       0.72      0.77      0.74       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.65      0.72       402
           1       0.71      0.85      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.74       804
weighted avg       0.76      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.61      0.68       402
           1       0.68      0.83      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.71       804
weighted avg       0.73      0.72      0.71       804

Tested: n_clusters=4, k_neighbours=15, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7445
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.66      0.73       402
           1       0.72      0.85      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.75       804
weighted avg       0.77      0.76      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.84      0.67      0.74       402
           1       0.72      0.87      0.79       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.73      0.66      0.69       402
           1       0.69      0.76      0.72       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.64      0.72       402
           1       0.71      0.86      0.78       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.64      0.70       402
           1       0.69      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Tested: n_clusters=4, k_neighbours=15, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7418
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.65      0.71       402
           1       0.70      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.66      0.73       402
           1       0.71      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.64      0.71       402
           1       0.70      0.84      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.73       804
weighted avg       0.75      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.64      0.71       402
           1       0.70      0.84      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Tested: n_clusters=4, k_neighbours=15, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7393
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.67      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.68      0.74       402
           1       0.73      0.85      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.80      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.64      0.71       402
           1       0.70      0.85      0.77       402

    accuracy                           0.74       804
   macro avg       0.76      0.74      0.74       804
weighted avg       0.76      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.65      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=4, k_neighbours=17, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7425
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.72      0.75       402
           1       0.74      0.80      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.68      0.74       402
           1       0.72      0.85      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.64      0.70       402
           1       0.69      0.80      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.63      0.71       402
           1       0.70      0.85      0.77       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.66      0.71       402
           1       0.70      0.79      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Tested: n_clusters=4, k_neighbours=17, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7418
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.65      0.71       402
           1       0.70      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.72      0.76       402
           1       0.75      0.83      0.79       402

    accuracy                           0.78       804
   macro avg       0.78      0.78      0.78       804
weighted avg       0.78      0.78      0.78       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.65      0.71       402
           1       0.70      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.65      0.71       402
           1       0.70      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.67      0.71       402
           1       0.71      0.79      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Tested: n_clusters=4, k_neighbours=17, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7435
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.68      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.71      0.76       402
           1       0.75      0.84      0.79       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.71       402
           1       0.70      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.80      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=4, k_neighbours=19, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7453
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.70      0.76       402
           1       0.74      0.85      0.79       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.74      0.68      0.71       402
           1       0.71      0.76      0.73       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.67      0.73       402
           1       0.71      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.64      0.70       402
           1       0.69      0.80      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=4, k_neighbours=19, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7410
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.66      0.73       402
           1       0.71      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.69      0.74       402
           1       0.73      0.83      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.80      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.64      0.71       402
           1       0.70      0.84      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.60      0.69       402
           1       0.68      0.84      0.75       402

    accuracy                           0.72       804
   macro avg       0.74      0.72      0.72       804
weighted avg       0.74      0.72      0.72       804

Tested: n_clusters=4, k_neighbours=19, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7400
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.64      0.70       402
           1       0.69      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.70      0.74       402
           1       0.73      0.82      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.68      0.72       402
           1       0.71      0.79      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.66      0.73       402
           1       0.71      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.61      0.69       402
           1       0.69      0.85      0.76       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=5, k_neighbours=3, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7405
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.64      0.70       402
           1       0.70      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.66      0.73       402
           1       0.71      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.64      0.69       402
           1       0.69      0.79      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.71       804
weighted avg       0.72      0.72      0.71       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.68      0.74       402
           1       0.73      0.85      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.60      0.68       402
           1       0.67      0.83      0.74       402

    accuracy                           0.71       804
   macro avg       0.73      0.71      0.71       804
weighted avg       0.73      0.71      0.71       804

Tested: n_clusters=5, k_neighbours=3, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7351
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.64      0.69       402
           1       0.69      0.80      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.67      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.64      0.70       402
           1       0.69      0.80      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.64      0.71       402
           1       0.70      0.84      0.77       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.62      0.69       402
           1       0.69      0.83      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.72       804
weighted avg       0.74      0.73      0.72       804

Tested: n_clusters=5, k_neighbours=3, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7306
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.64      0.70       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.65      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.65      0.70       402
           1       0.69      0.79      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.66      0.73       402
           1       0.71      0.85      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.62      0.69       402
           1       0.68      0.82      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.71       804
weighted avg       0.73      0.72      0.71       804

Tested: n_clusters=5, k_neighbours=5, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7303
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.83      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.74      0.64      0.69       402
           1       0.68      0.78      0.73       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.83      0.67      0.75       402
           1       0.73      0.87      0.79       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.62      0.69       402
           1       0.69      0.83      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=5, k_neighbours=5, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7373
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.66      0.72       402
           1       0.71      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.64      0.71       402
           1       0.70      0.84      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.73      0.63      0.68       402
           1       0.68      0.77      0.72       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.66      0.73       402
           1       0.71      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.63      0.69       402
           1       0.69      0.81      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=5, k_neighbours=5, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7316
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.65      0.71       402
           1       0.70      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.71       402
           1       0.70      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.83      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.68      0.73       402
           1       0.72      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.62      0.68       402
           1       0.68      0.80      0.73       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Tested: n_clusters=5, k_neighbours=7, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7363
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.68      0.73       402
           1       0.72      0.82      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.65      0.70       402
           1       0.69      0.80      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.67      0.71       402
           1       0.70      0.79      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.68      0.73       402
           1       0.72      0.82      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.63      0.69       402
           1       0.69      0.80      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: n_clusters=5, k_neighbours=7, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7336
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.68      0.73       402
           1       0.72      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.67      0.71       402
           1       0.70      0.79      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.67      0.72       402
           1       0.71      0.80      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.65      0.71       402
           1       0.70      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.60      0.67       402
           1       0.67      0.81      0.73       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.70       804
weighted avg       0.72      0.71      0.70       804

Tested: n_clusters=5, k_neighbours=7, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7316
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.68      0.73       402
           1       0.72      0.82      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.70      0.75       402
           1       0.73      0.82      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.64      0.70       402
           1       0.69      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Tested: n_clusters=5, k_neighbours=9, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7443
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.66      0.71       402
           1       0.70      0.79      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.69      0.73       402
           1       0.72      0.80      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.64      0.71       402
           1       0.70      0.82      0.76       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.67      0.73       402
           1       0.72      0.85      0.78       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.75       804
weighted avg       0.76      0.76      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.64      0.70       402
           1       0.69      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Tested: n_clusters=5, k_neighbours=9, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7373
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.65      0.70       402
           1       0.69      0.79      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.68      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.67      0.71       402
           1       0.71      0.79      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.66      0.72       402
           1       0.71      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.59      0.67       402
           1       0.67      0.83      0.74       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.70       804
weighted avg       0.72      0.71      0.70       804

Tested: n_clusters=5, k_neighbours=9, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7303
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.69      0.72       402
           1       0.71      0.79      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.71      0.75       402
           1       0.74      0.82      0.77       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.69      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.68      0.74       402
           1       0.72      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.80      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Tested: n_clusters=5, k_neighbours=11, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7468
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.65      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.68      0.74       402
           1       0.72      0.84      0.78       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.64      0.71       402
           1       0.70      0.84      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.62      0.69       402
           1       0.68      0.82      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.71       804
weighted avg       0.73      0.72      0.71       804

Tested: n_clusters=5, k_neighbours=11, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7378
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.67      0.73       402
           1       0.71      0.82      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.71      0.76       402
           1       0.75      0.84      0.79       402

    accuracy                           0.78       804
   macro avg       0.78      0.78      0.78       804
weighted avg       0.78      0.78      0.78       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.70      0.73       402
           1       0.72      0.79      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.68      0.75       402
           1       0.73      0.85      0.78       402

    accuracy                           0.77       804
   macro avg       0.77      0.77      0.76       804
weighted avg       0.77      0.77      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.61      0.68       402
           1       0.68      0.82      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.71       804
weighted avg       0.73      0.72      0.71       804

Tested: n_clusters=5, k_neighbours=11, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7500
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.67      0.71       402
           1       0.71      0.79      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.67      0.73       402
           1       0.71      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.67      0.72       402
           1       0.71      0.80      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.71      0.81      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.64      0.70       402
           1       0.69      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Tested: n_clusters=5, k_neighbours=13, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7348
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.72      0.76       402
           1       0.74      0.83      0.78       402

    accuracy                           0.77       804
   macro avg       0.77      0.77      0.77       804
weighted avg       0.77      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.66      0.73       402
           1       0.72      0.85      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.64      0.70       402
           1       0.69      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=5, k_neighbours=13, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7485
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.70      0.74       402
           1       0.73      0.81      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.69      0.75       402
           1       0.73      0.84      0.78       402

    accuracy                           0.77       804
   macro avg       0.77      0.77      0.76       804
weighted avg       0.77      0.77      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.67      0.71       402
           1       0.71      0.78      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.63      0.70       402
           1       0.69      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Tested: n_clusters=5, k_neighbours=13, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7418
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.68      0.74       402
           1       0.72      0.83      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.75       804
weighted avg       0.76      0.76      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.83      0.67      0.74       402
           1       0.72      0.87      0.79       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.63      0.70       402
           1       0.69      0.83      0.76       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.60      0.70       402
           1       0.69      0.87      0.77       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.73       804
weighted avg       0.75      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.72       402
           1       0.71      0.81      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Tested: n_clusters=5, k_neighbours=15, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7453
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.83      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.68      0.73       402
           1       0.72      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.69      0.72       402
           1       0.71      0.78      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.64      0.72       402
           1       0.70      0.85      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.74       804
weighted avg       0.76      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.61      0.68       402
           1       0.68      0.82      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.71       804
weighted avg       0.73      0.72      0.71       804

Tested: n_clusters=5, k_neighbours=15, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7388
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.83      0.66      0.73       402
           1       0.72      0.86      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.68      0.74       402
           1       0.72      0.83      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.75       804
weighted avg       0.76      0.76      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.63      0.70       402
           1       0.69      0.83      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.72       804
weighted avg       0.74      0.73      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.64      0.71       402
           1       0.70      0.83      0.76       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=5, k_neighbours=15, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7435
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.65      0.72       402
           1       0.71      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.69      0.74       402
           1       0.73      0.83      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.62      0.69       402
           1       0.68      0.82      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.82      0.64      0.72       402
           1       0.70      0.86      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.74       804
weighted avg       0.76      0.75      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.60      0.68       402
           1       0.68      0.83      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.71       804
weighted avg       0.73      0.72      0.71       804

Tested: n_clusters=5, k_neighbours=17, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7378
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.68      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.68      0.73       402
           1       0.72      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.80      0.65      0.72       402
           1       0.71      0.84      0.77       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.60      0.69       402
           1       0.68      0.86      0.76       402

    accuracy                           0.73       804
   macro avg       0.75      0.73      0.73       804
weighted avg       0.75      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.76      0.62      0.68       402
           1       0.68      0.81      0.74       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Tested: n_clusters=5, k_neighbours=17, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7363
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.68      0.73       402
           1       0.72      0.82      0.77       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.68      0.73       402
           1       0.72      0.82      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.61      0.68       402
           1       0.68      0.82      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.71       804
weighted avg       0.73      0.72      0.71       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.64      0.71       402
           1       0.70      0.85      0.77       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.65      0.70       402
           1       0.70      0.80      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Tested: n_clusters=5, k_neighbours=17, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7378
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.69      0.73       402
           1       0.72      0.80      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.70      0.73       402
           1       0.72      0.79      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.63      0.70       402
           1       0.69      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.63      0.70       402
           1       0.69      0.84      0.76       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: n_clusters=5, k_neighbours=19, metric=<function euclidean at 0x123ab1e50> --> Acc: 0.7363
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.70      0.74       402
           1       0.73      0.79      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.68      0.74       402
           1       0.72      0.84      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.74      0.67      0.71       402
           1       0.70      0.77      0.73       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.81      0.62      0.70       402
           1       0.69      0.85      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.73       804
weighted avg       0.75      0.74      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.75      0.64      0.69       402
           1       0.69      0.79      0.73       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.71       804
weighted avg       0.72      0.72      0.71       804

Tested: n_clusters=5, k_neighbours=19, metric=<function cityblock at 0x123ac1310> --> Acc: 0.7361
Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.63      0.70       402
           1       0.69      0.83      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.79      0.69      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.77      0.62      0.69       402
           1       0.69      0.82      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

           0       0.78      0.62      0.70       402
           1       0.69      0.83      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.72       804
weighted avg       0.74      0.73      0.72       804

Tested: n_clusters=5, k_neighbours=19, metric=<function weighted_euclidean at 0x13abe0940> --> Acc: 0.7333

 Best Params for dc_knn_combining_algorithm: (2, 13, <function euclidean at 0x123ab1e50>) → Accuracy: 0.755

best_params_knn = hyperparameter_tune(
    model=knn,  
    X_train=X_train_reduced,
    y_train=y_resampled,
    param_grid=param_grid
)

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.79      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.78      0.70      0.74       402
           1       0.73      0.81      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.75      0.67      0.71       402
           1       0.70      0.78      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.81      0.67      0.73       402
           1       0.72      0.84      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.78      0.64      0.70       402
           1       0.70      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Tested: k_neighbours=3 --> Acc: 0.7405
Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.80      0.67      0.73       402
           1       0.72      0.83      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.75      0.67      0.71       402
           1       0.70      0.78      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.81      0.69      0.74       402
           1       0.73      0.84      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.76      0.64      0.69       402
           1       0.69      0.79      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.71       804
weighted avg       0.72      0.72      0.71       804

Tested: k_neighbours=5 --> Acc: 0.7391
Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.78      0.69      0.73       402
           1       0.72      0.80      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.78      0.69      0.73       402
           1       0.72      0.80      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.78      0.70      0.74       402
           1       0.73      0.80      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.77      0.69      0.73       402
           1       0.72      0.80      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.77      0.65      0.70       402
           1       0.70      0.80      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Tested: k_neighbours=7 --> Acc: 0.7418
Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.79      0.67      0.73       402
           1       0.71      0.82      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.79      0.69      0.74       402
           1       0.73      0.82      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.76      0.70      0.73       402
           1       0.72      0.78      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.78      0.68      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.77      0.64      0.70       402
           1       0.69      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Tested: k_neighbours=9 --> Acc: 0.7423
Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.81      0.71      0.76       402
           1       0.74      0.84      0.79       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.78      0.70      0.74       402
           1       0.73      0.81      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.80      0.69      0.74       402
           1       0.73      0.83      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.77      0.64      0.70       402
           1       0.69      0.80      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: k_neighbours=11 --> Acc: 0.7505
Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.79      0.69      0.73       402
           1       0.72      0.81      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.80      0.71      0.75       402
           1       0.74      0.83      0.78       402

    accuracy                           0.77       804
   macro avg       0.77      0.77      0.77       804
weighted avg       0.77      0.77      0.77       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.79      0.70      0.74       402
           1       0.73      0.82      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.80      0.69      0.74       402
           1       0.73      0.83      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.77      0.63      0.69       402
           1       0.69      0.81      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Tested: k_neighbours=13 --> Acc: 0.7515
Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.80      0.69      0.74       402
           1       0.73      0.82      0.77       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.80      0.72      0.76       402
           1       0.75      0.81      0.78       402

    accuracy                           0.77       804
   macro avg       0.77      0.77      0.77       804
weighted avg       0.77      0.77      0.77       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.79      0.68      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.81      0.68      0.74       402
           1       0.72      0.84      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.78      0.66      0.72       402
           1       0.71      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.75      0.74      0.74       804
weighted avg       0.75      0.74      0.74       804

Tested: k_neighbours=15 --> Acc: 0.7547
Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.81      0.69      0.75       402
           1       0.73      0.83      0.78       402

    accuracy                           0.76       804
   macro avg       0.77      0.76      0.76       804
weighted avg       0.77      0.76      0.76       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.80      0.72      0.76       402
           1       0.75      0.82      0.78       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.77      0.66      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.81      0.66      0.73       402
           1       0.71      0.85      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.79      0.65      0.71       402
           1       0.70      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Tested: k_neighbours=17 --> Acc: 0.7520
Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.81      0.69      0.75       402
           1       0.73      0.84      0.78       402

    accuracy                           0.77       804
   macro avg       0.77      0.77      0.77       804
weighted avg       0.77      0.77      0.77       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.81      0.72      0.76       402
           1       0.74      0.83      0.78       402

    accuracy                           0.77       804
   macro avg       0.78      0.77      0.77       804
weighted avg       0.78      0.77      0.77       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.78      0.67      0.72       402
           1       0.71      0.81      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.81      0.65      0.72       402
           1       0.71      0.85      0.77       402

    accuracy                           0.75       804
   macro avg       0.76      0.75      0.75       804
weighted avg       0.76      0.75      0.75       804

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

           0       0.78      0.65      0.71       402
           1       0.70      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Tested: k_neighbours=19 --> Acc: 0.7522

 Best Params for knn: (15,) → Accuracy: 0.7547

best_params_dc_knn_centorid, all_combos_dc_knn_cent = hyperparameter_tune(
    model=dc_knn_centroid_nn,  
    X_train=X_train_reduced,
    y_train=y_resampled,
    param_grid=param_grid
)

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.82      0.71       402
           1       0.74      0.50      0.60       402

    accuracy                           0.66       804
   macro avg       0.68      0.66      0.65       804
weighted avg       0.68      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.64      0.66       402
           1       0.67      0.71      0.69       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.41      0.53       402
           1       0.59      0.87      0.71       402

    accuracy                           0.64       804
   macro avg       0.68      0.64      0.62       804
weighted avg       0.68      0.64      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.36      0.48       402
           1       0.57      0.84      0.68       402

    accuracy                           0.60       804
   macro avg       0.63      0.60      0.58       804
weighted avg       0.63      0.60      0.58       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.48      0.58       402
           1       0.61      0.83      0.71       402

    accuracy                           0.65       804
   macro avg       0.68      0.65      0.64       804
weighted avg       0.68      0.65      0.64       804

Tested: n_clusters=2, k_neighbours=3, metric=<function euclidean at 0x123901e50> --> Acc: 0.6460
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.76      0.68       402
           1       0.68      0.51      0.58       402

    accuracy                           0.64       804
   macro avg       0.65      0.64      0.63       804
weighted avg       0.65      0.64      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.53      0.60       402
           1       0.62      0.77      0.69       402

    accuracy                           0.65       804
   macro avg       0.66      0.65      0.64       804
weighted avg       0.66      0.65      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.55      0.66      0.60       402
           1       0.58      0.47      0.51       402

    accuracy                           0.56       804
   macro avg       0.56      0.56      0.56       804
weighted avg       0.56      0.56      0.56       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.59      0.61      0.60       402
           1       0.60      0.57      0.58       402

    accuracy                           0.59       804
   macro avg       0.59      0.59      0.59       804
weighted avg       0.59      0.59      0.59       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.77      0.55      0.65       402
           1       0.65      0.84      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.69       804
weighted avg       0.71      0.70      0.69       804

Tested: n_clusters=2, k_neighbours=3, metric=<function cityblock at 0x123913310> --> Acc: 0.6266
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.79      0.70       402
           1       0.72      0.54      0.62       402

    accuracy                           0.66       804
   macro avg       0.68      0.66      0.66       804
weighted avg       0.68      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.63      0.66       402
           1       0.66      0.73      0.69       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.73      0.69       402
           1       0.69      0.60      0.64       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.51      0.57       402
           1       0.59      0.70      0.64       402

    accuracy                           0.61       804
   macro avg       0.61      0.61      0.60       804
weighted avg       0.61      0.61      0.60       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.47      0.56       402
           1       0.60      0.81      0.69       402

    accuracy                           0.64       804
   macro avg       0.66      0.64      0.63       804
weighted avg       0.66      0.64      0.63       804

Tested: n_clusters=2, k_neighbours=3, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6507
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.72      0.67       402
           1       0.67      0.57      0.62       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.64       804
weighted avg       0.65      0.65      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.66      0.68       402
           1       0.68      0.73      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.69       804
weighted avg       0.70      0.70      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.70      0.66       402
           1       0.66      0.58      0.62       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.52      0.59       402
           1       0.61      0.75      0.67       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.63       804
weighted avg       0.64      0.64      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.58      0.75      0.66       402
           1       0.65      0.46      0.54       402

    accuracy                           0.61       804
   macro avg       0.62      0.61      0.60       804
weighted avg       0.62      0.61      0.60       804

Tested: n_clusters=3, k_neighbours=3, metric=<function euclidean at 0x123901e50> --> Acc: 0.6445
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.62      0.64       402
           1       0.64      0.69      0.66       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.79      0.71       402
           1       0.73      0.57      0.64       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.68       804
weighted avg       0.69      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.59      0.78      0.67       402
           1       0.68      0.45      0.54       402

    accuracy                           0.62       804
   macro avg       0.63      0.62      0.61       804
weighted avg       0.63      0.62      0.61       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.66      0.67       402
           1       0.67      0.69      0.68       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.66      0.66       402
           1       0.66      0.66      0.66       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Tested: n_clusters=3, k_neighbours=3, metric=<function cityblock at 0x123913310> --> Acc: 0.6577
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.73      0.75       402
           1       0.74      0.77      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.73      0.71       402
           1       0.72      0.69      0.70       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.52      0.60       402
           1       0.62      0.79      0.70       402

    accuracy                           0.65       804
   macro avg       0.67      0.65      0.65       804
weighted avg       0.67      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.73      0.68       402
           1       0.68      0.59      0.63       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.71      0.67       402
           1       0.67      0.59      0.63       402

    accuracy                           0.65       804
   macro avg       0.66      0.65      0.65       804
weighted avg       0.66      0.65      0.65       804

Tested: n_clusters=3, k_neighbours=3, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6856
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.41      0.52       402
           1       0.58      0.83      0.69       402

    accuracy                           0.62       804
   macro avg       0.64      0.62      0.60       804
weighted avg       0.64      0.62      0.60       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.55      0.75      0.63       402
           1       0.60      0.38      0.47       402

    accuracy                           0.57       804
   macro avg       0.58      0.57      0.55       804
weighted avg       0.58      0.57      0.55       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.77      0.68       402
           1       0.69      0.50      0.58       402

    accuracy                           0.64       804
   macro avg       0.65      0.64      0.63       804
weighted avg       0.65      0.64      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.66      0.64       402
           1       0.64      0.60      0.62       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.56      0.60       402
           1       0.61      0.68      0.64       402

    accuracy                           0.62       804
   macro avg       0.62      0.62      0.62       804
weighted avg       0.62      0.62      0.62       804

Tested: n_clusters=3, k_neighbours=5, metric=<function euclidean at 0x123901e50> --> Acc: 0.6142
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.51      0.57       402
           1       0.59      0.71      0.64       402

    accuracy                           0.61       804
   macro avg       0.61      0.61      0.61       804
weighted avg       0.61      0.61      0.61       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.57      0.70      0.63       402
           1       0.61      0.46      0.52       402

    accuracy                           0.58       804
   macro avg       0.59      0.58      0.57       804
weighted avg       0.59      0.58      0.57       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.60      0.54      0.57       402
           1       0.58      0.64      0.61       402

    accuracy                           0.59       804
   macro avg       0.59      0.59      0.59       804
weighted avg       0.59      0.59      0.59       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.56      0.72      0.63       402
           1       0.61      0.43      0.50       402

    accuracy                           0.57       804
   macro avg       0.58      0.57      0.56       804
weighted avg       0.58      0.57      0.56       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.54      0.69      0.60       402
           1       0.57      0.41      0.47       402

    accuracy                           0.55       804
   macro avg       0.55      0.55      0.54       804
weighted avg       0.55      0.55      0.54       804

Tested: n_clusters=3, k_neighbours=5, metric=<function cityblock at 0x123913310> --> Acc: 0.5806
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.42      0.54       402
           1       0.60      0.86      0.71       402

    accuracy                           0.64       804
   macro avg       0.68      0.64      0.62       804
weighted avg       0.68      0.64      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.70      0.67       402
           1       0.67      0.60      0.64       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.60      0.69      0.64       402
           1       0.63      0.53      0.58       402

    accuracy                           0.61       804
   macro avg       0.62      0.61      0.61       804
weighted avg       0.62      0.61      0.61       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.62      0.67       402
           1       0.67      0.79      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.58      0.80      0.67       402
           1       0.68      0.42      0.52       402

    accuracy                           0.61       804
   macro avg       0.63      0.61      0.60       804
weighted avg       0.63      0.61      0.60       804

Tested: n_clusters=3, k_neighbours=5, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6443
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.78      0.71       402
           1       0.73      0.60      0.66       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.68      0.70       402
           1       0.69      0.72      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.73      0.69       402
           1       0.70      0.62      0.66       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.67       804
weighted avg       0.68      0.68      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.62      0.65       402
           1       0.66      0.73      0.69       402

    accuracy                           0.67       804
   macro avg       0.68      0.67      0.67       804
weighted avg       0.68      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.56      0.64       402
           1       0.65      0.81      0.72       402

    accuracy                           0.69       804
   macro avg       0.70      0.69      0.68       804
weighted avg       0.70      0.69      0.68       804

Tested: n_clusters=4, k_neighbours=3, metric=<function euclidean at 0x123901e50> --> Acc: 0.6851
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.66      0.65       402
           1       0.65      0.62      0.63       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.65      0.65       402
           1       0.65      0.66      0.66       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.76      0.68       402
           1       0.69      0.53      0.60       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.64       804
weighted avg       0.65      0.65      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.76      0.69       402
           1       0.70      0.57      0.63       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.66       804
weighted avg       0.67      0.67      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.61      0.65       402
           1       0.65      0.73      0.69       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Tested: n_clusters=4, k_neighbours=3, metric=<function cityblock at 0x123913310> --> Acc: 0.6547
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.74      0.70       402
           1       0.71      0.63      0.67       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.78      0.73       402
           1       0.74      0.65      0.69       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.68      0.65       402
           1       0.65      0.60      0.63       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.63      0.66       402
           1       0.66      0.71      0.68       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.70      0.70       402
           1       0.70      0.70      0.70       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Tested: n_clusters=4, k_neighbours=3, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6828
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.67      0.64       402
           1       0.63      0.57      0.60       402

    accuracy                           0.62       804
   macro avg       0.62      0.62      0.62       804
weighted avg       0.62      0.62      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.57      0.59       402
           1       0.60      0.64      0.62       402

    accuracy                           0.61       804
   macro avg       0.61      0.61      0.61       804
weighted avg       0.61      0.61      0.61       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.64      0.69       402
           1       0.69      0.79      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.71       804
weighted avg       0.72      0.72      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.75      0.72       402
           1       0.73      0.67      0.70       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.58      0.70      0.63       402
           1       0.62      0.50      0.55       402

    accuracy                           0.60       804
   macro avg       0.60      0.60      0.59       804
weighted avg       0.60      0.60      0.59       804

Tested: n_clusters=4, k_neighbours=5, metric=<function euclidean at 0x123901e50> --> Acc: 0.6498
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.64      0.64       402
           1       0.64      0.64      0.64       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.63      0.65       402
           1       0.66      0.71      0.68       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.56      0.77      0.65       402
           1       0.64      0.40      0.49       402

    accuracy                           0.59       804
   macro avg       0.60      0.59      0.57       804
weighted avg       0.60      0.59      0.57       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.61      0.62       402
           1       0.63      0.66      0.64       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.64      0.66       402
           1       0.66      0.70      0.68       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Tested: n_clusters=4, k_neighbours=5, metric=<function cityblock at 0x123913310> --> Acc: 0.6400
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.58      0.61       402
           1       0.62      0.68      0.65       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.71      0.66       402
           1       0.66      0.57      0.61       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.70      0.68       402
           1       0.68      0.64      0.66       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.70      0.68       402
           1       0.68      0.63      0.66       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.74      0.69       402
           1       0.69      0.59      0.64       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.66       804
weighted avg       0.67      0.67      0.66       804

Tested: n_clusters=4, k_neighbours=5, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6545
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.56      0.60       402
           1       0.61      0.69      0.65       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.68      0.67       402
           1       0.68      0.67      0.67       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.36      0.48       402
           1       0.57      0.86      0.69       402

    accuracy                           0.61       804
   macro avg       0.64      0.61      0.58       804
weighted avg       0.64      0.61      0.58       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.69      0.65       402
           1       0.65      0.57      0.61       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.57      0.67      0.62       402
           1       0.60      0.50      0.55       402

    accuracy                           0.59       804
   macro avg       0.59      0.59      0.58       804
weighted avg       0.59      0.59      0.58       804

Tested: n_clusters=4, k_neighbours=7, metric=<function euclidean at 0x123901e50> --> Acc: 0.6261
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.54      0.76      0.63       402
           1       0.60      0.36      0.45       402

    accuracy                           0.56       804
   macro avg       0.57      0.56      0.54       804
weighted avg       0.57      0.56      0.54       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.76      0.68       402
           1       0.69      0.53      0.60       402

    accuracy                           0.64       804
   macro avg       0.65      0.64      0.64       804
weighted avg       0.65      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.55      0.59       402
           1       0.61      0.69      0.65       402

    accuracy                           0.62       804
   macro avg       0.62      0.62      0.62       804
weighted avg       0.62      0.62      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.59      0.59      0.59       402
           1       0.59      0.59      0.59       402

    accuracy                           0.59       804
   macro avg       0.59      0.59      0.59       804
weighted avg       0.59      0.59      0.59       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.58      0.55      0.56       402
           1       0.57      0.60      0.59       402

    accuracy                           0.57       804
   macro avg       0.57      0.57      0.57       804
weighted avg       0.57      0.57      0.57       804

Tested: n_clusters=4, k_neighbours=7, metric=<function cityblock at 0x123913310> --> Acc: 0.5983
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.57      0.73      0.64       402
           1       0.62      0.46      0.53       402

    accuracy                           0.59       804
   macro avg       0.60      0.59      0.58       804
weighted avg       0.60      0.59      0.58       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.57      0.83      0.68       402
           1       0.70      0.38      0.49       402

    accuracy                           0.61       804
   macro avg       0.64      0.61      0.59       804
weighted avg       0.64      0.61      0.59       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.60      0.79      0.68       402
           1       0.69      0.48      0.56       402

    accuracy                           0.63       804
   macro avg       0.65      0.63      0.62       804
weighted avg       0.65      0.63      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.69      0.66       402
           1       0.65      0.59      0.62       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.77      0.57      0.66       402
           1       0.66      0.83      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.69       804
weighted avg       0.71      0.70      0.69       804

Tested: n_clusters=4, k_neighbours=7, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6341
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.53      0.62       402
           1       0.63      0.81      0.71       402

    accuracy                           0.67       804
   macro avg       0.69      0.67      0.67       804
weighted avg       0.69      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.74      0.72       402
           1       0.73      0.70      0.71       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.69      0.71       402
           1       0.71      0.74      0.72       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.67      0.70       402
           1       0.70      0.77      0.73       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.62      0.65       402
           1       0.65      0.71      0.68       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Tested: n_clusters=5, k_neighbours=3, metric=<function euclidean at 0x123901e50> --> Acc: 0.6983
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.66      0.68       402
           1       0.68      0.71      0.69       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.61      0.65       402
           1       0.65      0.73      0.69       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.56      0.63       402
           1       0.64      0.79      0.71       402

    accuracy                           0.67       804
   macro avg       0.68      0.67      0.67       804
weighted avg       0.68      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.70      0.67       402
           1       0.67      0.61      0.64       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.65       804
weighted avg       0.66      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.62      0.63       402
           1       0.64      0.66      0.65       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Tested: n_clusters=5, k_neighbours=3, metric=<function cityblock at 0x123913310> --> Acc: 0.6657
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.66      0.69       402
           1       0.69      0.74      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.68      0.69       402
           1       0.69      0.70      0.70       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.81      0.74       402
           1       0.76      0.61      0.68       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.65      0.68       402
           1       0.68      0.72      0.70       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.61      0.66       402
           1       0.66      0.77      0.71       402

    accuracy                           0.69       804
   macro avg       0.70      0.69      0.69       804
weighted avg       0.70      0.69      0.69       804

Tested: n_clusters=5, k_neighbours=3, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6968
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.62      0.65       402
           1       0.65      0.70      0.68       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.73      0.69       402
           1       0.70      0.62      0.66       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.72      0.69       402
           1       0.69      0.63      0.66       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.61      0.66       402
           1       0.66      0.75      0.70       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.68       804
weighted avg       0.69      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.64      0.68       402
           1       0.68      0.77      0.72       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Tested: n_clusters=5, k_neighbours=5, metric=<function euclidean at 0x123901e50> --> Acc: 0.6803
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.66      0.67       402
           1       0.67      0.69      0.68       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.69      0.65       402
           1       0.65      0.57      0.61       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.74      0.69       402
           1       0.69      0.59      0.64       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.66       804
weighted avg       0.67      0.67      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.64      0.67       402
           1       0.67      0.71      0.69       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.62      0.63       402
           1       0.64      0.66      0.65       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Tested: n_clusters=5, k_neighbours=5, metric=<function cityblock at 0x123913310> --> Acc: 0.6572
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.77      0.72       402
           1       0.74      0.65      0.69       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.82      0.72       402
           1       0.75      0.52      0.62       402

    accuracy                           0.67       804
   macro avg       0.69      0.67      0.67       804
weighted avg       0.69      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.56      0.63       402
           1       0.64      0.77      0.70       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.66       804
weighted avg       0.67      0.67      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.72      0.69       402
           1       0.70      0.64      0.67       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.77      0.52      0.62       402
           1       0.64      0.85      0.73       402

    accuracy                           0.68       804
   macro avg       0.70      0.68      0.67       804
weighted avg       0.70      0.68      0.67       804

Tested: n_clusters=5, k_neighbours=5, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6823
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.69      0.70       402
           1       0.70      0.71      0.70       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.68      0.66       402
           1       0.66      0.63      0.65       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.71      0.67       402
           1       0.67      0.61      0.64       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.67      0.69       402
           1       0.69      0.72      0.70       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.48      0.56       402
           1       0.60      0.79      0.68       402

    accuracy                           0.63       804
   macro avg       0.65      0.63      0.62       804
weighted avg       0.65      0.63      0.62       804

Tested: n_clusters=5, k_neighbours=7, metric=<function euclidean at 0x123901e50> --> Acc: 0.6682
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.60      0.68      0.64       402
           1       0.63      0.55      0.59       402

    accuracy                           0.61       804
   macro avg       0.62      0.61      0.61       804
weighted avg       0.62      0.61      0.61       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.53      0.83      0.65       402
           1       0.62      0.28      0.39       402

    accuracy                           0.55       804
   macro avg       0.58      0.55      0.52       804
weighted avg       0.58      0.55      0.52       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.54      0.78      0.64       402
           1       0.60      0.33      0.43       402

    accuracy                           0.56       804
   macro avg       0.57      0.56      0.53       804
weighted avg       0.57      0.56      0.53       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.57      0.73      0.64       402
           1       0.62      0.44      0.51       402

    accuracy                           0.58       804
   macro avg       0.59      0.58      0.58       804
weighted avg       0.59      0.58      0.58       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.55      0.64      0.59       402
           1       0.56      0.47      0.51       402

    accuracy                           0.55       804
   macro avg       0.56      0.55      0.55       804
weighted avg       0.56      0.55      0.55       804

Tested: n_clusters=5, k_neighbours=7, metric=<function cityblock at 0x123913310> --> Acc: 0.5724
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.52      0.60       402
           1       0.62      0.79      0.70       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.65       804
weighted avg       0.67      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.77      0.71       402
           1       0.73      0.60      0.65       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.68       804
weighted avg       0.69      0.69      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.70      0.67       402
           1       0.67      0.60      0.63       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.57      0.85      0.68       402
           1       0.70      0.35      0.47       402

    accuracy                           0.60       804
   macro avg       0.64      0.60      0.58       804
weighted avg       0.64      0.60      0.58       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.79      0.39      0.52       402
           1       0.59      0.90      0.72       402

    accuracy                           0.64       804
   macro avg       0.69      0.64      0.62       804
weighted avg       0.69      0.64      0.62       804

Tested: n_clusters=5, k_neighbours=7, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6473
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.72      0.66       402
           1       0.66      0.55      0.60       402

    accuracy                           0.63       804
   macro avg       0.64      0.63      0.63       804
weighted avg       0.64      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.73      0.68       402
           1       0.69      0.58      0.63       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.65       804
weighted avg       0.66      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.66      0.68       402
           1       0.68      0.72      0.70       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.65      0.66       402
           1       0.66      0.68      0.67       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.50      0.56       402
           1       0.59      0.72      0.65       402

    accuracy                           0.61       804
   macro avg       0.62      0.61      0.60       804
weighted avg       0.62      0.61      0.60       804

Tested: n_clusters=5, k_neighbours=9, metric=<function euclidean at 0x123901e50> --> Acc: 0.6510
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.41      0.52       402
           1       0.58      0.84      0.69       402

    accuracy                           0.62       804
   macro avg       0.65      0.62      0.60       804
weighted avg       0.65      0.62      0.60       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.60      0.64       402
           1       0.64      0.72      0.68       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.65      0.63       402
           1       0.63      0.60      0.61       402

    accuracy                           0.62       804
   macro avg       0.62      0.62      0.62       804
weighted avg       0.62      0.62      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.67      0.65       402
           1       0.65      0.61      0.63       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.58      0.79      0.67       402
           1       0.66      0.42      0.51       402

    accuracy                           0.60       804
   macro avg       0.62      0.60      0.59       804
weighted avg       0.62      0.60      0.59       804

Tested: n_clusters=5, k_neighbours=9, metric=<function cityblock at 0x123913310> --> Acc: 0.6303
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.65      0.63       402
           1       0.63      0.58      0.60       402

    accuracy                           0.62       804
   macro avg       0.62      0.62      0.62       804
weighted avg       0.62      0.62      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.68      0.66       402
           1       0.66      0.62      0.64       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.67      0.65       402
           1       0.65      0.60      0.62       402

    accuracy                           0.63       804
   macro avg       0.64      0.63      0.63       804
weighted avg       0.64      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.49      0.58       402
           1       0.62      0.83      0.71       402

    accuracy                           0.66       804
   macro avg       0.68      0.66      0.65       804
weighted avg       0.68      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.56      0.52      0.54       402
           1       0.55      0.60      0.58       402

    accuracy                           0.56       804
   macro avg       0.56      0.56      0.56       804
weighted avg       0.56      0.56      0.56       804

Tested: n_clusters=5, k_neighbours=9, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6226
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.73      0.70       402
           1       0.70      0.63      0.67       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.68      0.72       402
           1       0.71      0.79      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.70      0.73       402
           1       0.72      0.78      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.72      0.70       402
           1       0.70      0.65      0.68       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.62      0.67       402
           1       0.67      0.77      0.72       402

    accuracy                           0.69       804
   macro avg       0.70      0.69      0.69       804
weighted avg       0.70      0.69      0.69       804

Tested: n_clusters=6, k_neighbours=3, metric=<function euclidean at 0x123901e50> --> Acc: 0.7072
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.77      0.71       402
           1       0.72      0.58      0.64       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.67       804
weighted avg       0.68      0.68      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.58      0.63       402
           1       0.64      0.76      0.69       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.66       804
weighted avg       0.67      0.67      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.67      0.67       402
           1       0.67      0.69      0.68       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.76      0.69       402
           1       0.70      0.56      0.62       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.66       804
weighted avg       0.67      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.60      0.69      0.64       402
           1       0.64      0.54      0.58       402

    accuracy                           0.62       804
   macro avg       0.62      0.62      0.61       804
weighted avg       0.62      0.62      0.61       804

Tested: n_clusters=6, k_neighbours=3, metric=<function cityblock at 0x123913310> --> Acc: 0.6595
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.70      0.70       402
           1       0.70      0.70      0.70       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.75      0.73       402
           1       0.73      0.68      0.70       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.71       804
weighted avg       0.72      0.72      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.70      0.71       402
           1       0.71      0.72      0.72       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.68      0.68       402
           1       0.68      0.67      0.67       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.78      0.54      0.64       402
           1       0.65      0.85      0.74       402

    accuracy                           0.70       804
   macro avg       0.72      0.70      0.69       804
weighted avg       0.72      0.70      0.69       804

Tested: n_clusters=6, k_neighbours=3, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.7005
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.61      0.63       402
           1       0.64      0.69      0.66       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.80      0.72       402
           1       0.74      0.57      0.64       402

    accuracy                           0.69       804
   macro avg       0.70      0.69      0.68       804
weighted avg       0.70      0.69      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.79      0.59      0.68       402
           1       0.67      0.85      0.75       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.71       804
weighted avg       0.73      0.72      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.76      0.68       402
           1       0.69      0.54      0.61       402

    accuracy                           0.65       804
   macro avg       0.66      0.65      0.65       804
weighted avg       0.66      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.64      0.67       402
           1       0.67      0.75      0.71       402

    accuracy                           0.69       804
   macro avg       0.70      0.69      0.69       804
weighted avg       0.70      0.69      0.69       804

Tested: n_clusters=6, k_neighbours=5, metric=<function euclidean at 0x123901e50> --> Acc: 0.6789
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.70      0.69       402
           1       0.69      0.68      0.69       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.66      0.65       402
           1       0.65      0.63      0.64       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.54      0.61       402
           1       0.63      0.77      0.69       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.65       804
weighted avg       0.66      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.69      0.65       402
           1       0.65      0.57      0.61       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.73      0.67       402
           1       0.67      0.54      0.60       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.63       804
weighted avg       0.64      0.64      0.63       804

Tested: n_clusters=6, k_neighbours=5, metric=<function cityblock at 0x123913310> --> Acc: 0.6512
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.67      0.67       402
           1       0.67      0.67      0.67       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.72      0.70       402
           1       0.70      0.66      0.68       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.78      0.47      0.59       402
           1       0.62      0.87      0.72       402

    accuracy                           0.67       804
   macro avg       0.70      0.67      0.65       804
weighted avg       0.70      0.67      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.72      0.71       402
           1       0.71      0.68      0.69       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.77      0.57      0.65       402
           1       0.66      0.83      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.69       804
weighted avg       0.71      0.70      0.69       804

Tested: n_clusters=6, k_neighbours=5, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6856
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.67      0.69       402
           1       0.68      0.71      0.70       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.69      0.69       402
           1       0.69      0.70      0.70       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.60      0.64       402
           1       0.64      0.71      0.68       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.59      0.64       402
           1       0.65      0.76      0.70       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.67       804
weighted avg       0.68      0.68      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.59      0.68      0.63       402
           1       0.62      0.52      0.57       402

    accuracy                           0.60       804
   macro avg       0.61      0.60      0.60       804
weighted avg       0.61      0.60      0.60       804

Tested: n_clusters=6, k_neighbours=7, metric=<function euclidean at 0x123901e50> --> Acc: 0.6652
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.51      0.58       402
           1       0.60      0.75      0.67       402

    accuracy                           0.63       804
   macro avg       0.64      0.63      0.62       804
weighted avg       0.64      0.63      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.60      0.77      0.68       402
           1       0.68      0.50      0.57       402

    accuracy                           0.63       804
   macro avg       0.64      0.63      0.63       804
weighted avg       0.64      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.74      0.70       402
           1       0.71      0.63      0.67       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.68       804
weighted avg       0.69      0.69      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.62      0.63       402
           1       0.63      0.64      0.63       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.63      0.63       402
           1       0.63      0.63      0.63       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Tested: n_clusters=6, k_neighbours=7, metric=<function cityblock at 0x123913310> --> Acc: 0.6415
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.66      0.69       402
           1       0.69      0.74      0.72       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.75      0.71       402
           1       0.72      0.64      0.67       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.76      0.72       402
           1       0.73      0.65      0.69       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.57      0.63       402
           1       0.64      0.76      0.69       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.66       804
weighted avg       0.67      0.67      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.62      0.65       402
           1       0.66      0.72      0.69       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Tested: n_clusters=6, k_neighbours=7, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6873
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.71      0.68       402
           1       0.69      0.64      0.66       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.82      0.70       402
           1       0.73      0.48      0.57       402

    accuracy                           0.65       804
   macro avg       0.67      0.65      0.64       804
weighted avg       0.67      0.65      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.81      0.70       402
           1       0.72      0.50      0.59       402

    accuracy                           0.65       804
   macro avg       0.67      0.65      0.65       804
weighted avg       0.67      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.52      0.61       402
           1       0.63      0.81      0.71       402

    accuracy                           0.66       804
   macro avg       0.68      0.66      0.66       804
weighted avg       0.68      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.58      0.63       402
           1       0.64      0.74      0.69       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.66       804
weighted avg       0.67      0.66      0.66       804

Tested: n_clusters=6, k_neighbours=9, metric=<function euclidean at 0x123901e50> --> Acc: 0.6604
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.54      0.60       402
           1       0.62      0.74      0.67       402

    accuracy                           0.64       804
   macro avg       0.65      0.64      0.64       804
weighted avg       0.65      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.75      0.69       402
           1       0.70      0.58      0.64       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.66       804
weighted avg       0.67      0.67      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.62      0.62       402
           1       0.62      0.61      0.61       402

    accuracy                           0.62       804
   macro avg       0.62      0.62      0.62       804
weighted avg       0.62      0.62      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.63      0.66       402
           1       0.66      0.70      0.68       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.57      0.62       402
           1       0.63      0.72      0.67       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Tested: n_clusters=6, k_neighbours=9, metric=<function cityblock at 0x123913310> --> Acc: 0.6478
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.53      0.60       402
           1       0.61      0.74      0.67       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.63       804
weighted avg       0.64      0.64      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.76      0.69       402
           1       0.70      0.56      0.62       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.66       804
weighted avg       0.67      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.81      0.73       402
           1       0.76      0.59      0.66       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.68      0.68       402
           1       0.68      0.67      0.68       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.59      0.79      0.68       402
           1       0.69      0.45      0.55       402

    accuracy                           0.62       804
   macro avg       0.64      0.62      0.61       804
weighted avg       0.64      0.62      0.61       804

Tested: n_clusters=6, k_neighbours=9, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6597
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.40      0.51       402
           1       0.58      0.85      0.69       402

    accuracy                           0.62       804
   macro avg       0.65      0.62      0.60       804
weighted avg       0.65      0.62      0.60       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.70      0.67       402
           1       0.67      0.62      0.64       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.51      0.57       402
           1       0.59      0.71      0.64       402

    accuracy                           0.61       804
   macro avg       0.61      0.61      0.61       804
weighted avg       0.61      0.61      0.61       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.53      0.58       402
           1       0.60      0.69      0.64       402

    accuracy                           0.61       804
   macro avg       0.61      0.61      0.61       804
weighted avg       0.61      0.61      0.61       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.64      0.63       402
           1       0.63      0.60      0.62       402

    accuracy                           0.62       804
   macro avg       0.62      0.62      0.62       804
weighted avg       0.62      0.62      0.62       804

Tested: n_clusters=6, k_neighbours=11, metric=<function euclidean at 0x123901e50> --> Acc: 0.6246
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.45      0.52       402
           1       0.57      0.72      0.64       402

    accuracy                           0.59       804
   macro avg       0.59      0.59      0.58       804
weighted avg       0.59      0.59      0.58       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.60      0.72      0.66       402
           1       0.65      0.53      0.58       402

    accuracy                           0.62       804
   macro avg       0.63      0.62      0.62       804
weighted avg       0.63      0.62      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.60      0.65      0.63       402
           1       0.62      0.57      0.59       402

    accuracy                           0.61       804
   macro avg       0.61      0.61      0.61       804
weighted avg       0.61      0.61      0.61       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.59      0.60      0.59       402
           1       0.59      0.59      0.59       402

    accuracy                           0.59       804
   macro avg       0.59      0.59      0.59       804
weighted avg       0.59      0.59      0.59       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.56      0.71      0.63       402
           1       0.61      0.45      0.52       402

    accuracy                           0.58       804
   macro avg       0.59      0.58      0.57       804
weighted avg       0.59      0.58      0.57       804

Tested: n_clusters=6, k_neighbours=11, metric=<function cityblock at 0x123913310> --> Acc: 0.5993
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.66      0.68       402
           1       0.68      0.73      0.70       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.84      0.72       402
           1       0.76      0.51      0.61       402

    accuracy                           0.67       804
   macro avg       0.69      0.67      0.66       804
weighted avg       0.69      0.67      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.70      0.68       402
           1       0.68      0.64      0.66       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.53      0.61       402
           1       0.63      0.79      0.70       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.65       804
weighted avg       0.67      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.57      0.59       402
           1       0.59      0.63      0.61       402

    accuracy                           0.60       804
   macro avg       0.60      0.60      0.60       804
weighted avg       0.60      0.60      0.60       804

Tested: n_clusters=6, k_neighbours=11, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6592
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.71      0.72       402
           1       0.72      0.74      0.73       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.74      0.73       402
           1       0.73      0.71      0.72       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.73      0.72       402
           1       0.73      0.72      0.72       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.59      0.65       402
           1       0.66      0.78      0.72       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.68       804
weighted avg       0.69      0.69      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.62      0.67       402
           1       0.67      0.79      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Tested: n_clusters=7, k_neighbours=3, metric=<function euclidean at 0x123901e50> --> Acc: 0.7119
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.66      0.67       402
           1       0.67      0.70      0.69       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.56      0.62       402
           1       0.63      0.76      0.69       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.66       804
weighted avg       0.67      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.67      0.67       402
           1       0.67      0.66      0.67       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.64      0.66       402
           1       0.66      0.71      0.69       402

    accuracy                           0.67       804
   macro avg       0.68      0.67      0.67       804
weighted avg       0.68      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.61      0.66       402
           1       0.66      0.75      0.70       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Tested: n_clusters=7, k_neighbours=3, metric=<function cityblock at 0x123913310> --> Acc: 0.6726
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.64      0.68       402
           1       0.68      0.77      0.72       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.64      0.69       402
           1       0.69      0.79      0.73       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.71       804
weighted avg       0.72      0.72      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.70      0.71       402
           1       0.70      0.72      0.71       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.63      0.69       402
           1       0.69      0.80      0.74       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.70      0.70       402
           1       0.70      0.71      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Tested: n_clusters=7, k_neighbours=3, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.7095
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.77      0.65      0.71       402
           1       0.70      0.81      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.72      0.72       402
           1       0.72      0.72      0.72       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.65      0.69       402
           1       0.68      0.76      0.72       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.64      0.65       402
           1       0.65      0.65      0.65       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.67      0.71       402
           1       0.70      0.77      0.73       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Tested: n_clusters=7, k_neighbours=5, metric=<function euclidean at 0x123901e50> --> Acc: 0.7035
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.61      0.66       402
           1       0.66      0.76      0.71       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.68       804
weighted avg       0.69      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.64      0.65       402
           1       0.65      0.68      0.67       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.76      0.70       402
           1       0.71      0.60      0.65       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.69      0.67       402
           1       0.67      0.63      0.65       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.57      0.64       402
           1       0.65      0.78      0.71       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.67       804
weighted avg       0.69      0.68      0.67       804

Tested: n_clusters=7, k_neighbours=5, metric=<function cityblock at 0x123913310> --> Acc: 0.6719
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.78      0.69      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.68      0.68       402
           1       0.68      0.69      0.69       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.74      0.70       402
           1       0.71      0.63      0.67       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.68       804
weighted avg       0.69      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.71      0.71       402
           1       0.71      0.73      0.72       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.66      0.68       402
           1       0.67      0.71      0.69       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Tested: n_clusters=7, k_neighbours=5, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.7035
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.71      0.69       402
           1       0.70      0.67      0.68       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.60      0.77      0.67       402
           1       0.68      0.48      0.56       402

    accuracy                           0.63       804
   macro avg       0.64      0.63      0.62       804
weighted avg       0.64      0.63      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.69      0.68       402
           1       0.68      0.65      0.66       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.68      0.67       402
           1       0.67      0.65      0.66       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.65      0.69       402
           1       0.69      0.77      0.72       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Tested: n_clusters=7, k_neighbours=7, metric=<function euclidean at 0x123901e50> --> Acc: 0.6716
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.59      0.63       402
           1       0.64      0.74      0.69       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.66       804
weighted avg       0.67      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.72      0.67       402
           1       0.66      0.55      0.60       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.63       804
weighted avg       0.64      0.64      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.62      0.65       402
           1       0.65      0.70      0.67       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.75      0.70       402
           1       0.70      0.59      0.64       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.57      0.62       402
           1       0.62      0.71      0.66       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Tested: n_clusters=7, k_neighbours=7, metric=<function cityblock at 0x123913310> --> Acc: 0.6540
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.67      0.71       402
           1       0.70      0.79      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.75      0.68       402
           1       0.68      0.52      0.59       402

    accuracy                           0.64       804
   macro avg       0.65      0.64      0.63       804
weighted avg       0.65      0.64      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.58      0.66       402
           1       0.66      0.82      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.59      0.85      0.70       402
           1       0.74      0.42      0.53       402

    accuracy                           0.64       804
   macro avg       0.67      0.64      0.62       804
weighted avg       0.67      0.64      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.83      0.38      0.52       402
           1       0.60      0.92      0.72       402

    accuracy                           0.65       804
   macro avg       0.71      0.65      0.62       804
weighted avg       0.71      0.65      0.62       804

Tested: n_clusters=7, k_neighbours=7, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6706
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.59      0.62       402
           1       0.63      0.69      0.66       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.77      0.71       402
           1       0.72      0.60      0.65       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.68       804
weighted avg       0.69      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.51      0.60       402
           1       0.62      0.81      0.70       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.65       804
weighted avg       0.67      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.61      0.66       402
           1       0.66      0.76      0.70       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.68       804
weighted avg       0.69      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.78      0.49      0.60       402
           1       0.63      0.86      0.72       402

    accuracy                           0.67       804
   macro avg       0.70      0.67      0.66       804
weighted avg       0.70      0.67      0.66       804

Tested: n_clusters=7, k_neighbours=9, metric=<function euclidean at 0x123901e50> --> Acc: 0.6677
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.59      0.65       402
           1       0.65      0.78      0.71       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.68       804
weighted avg       0.69      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.73      0.69       402
           1       0.70      0.62      0.66       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.67       804
weighted avg       0.68      0.68      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.73      0.67       402
           1       0.67      0.54      0.60       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.63       804
weighted avg       0.64      0.64      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.59      0.67      0.63       402
           1       0.62      0.53      0.57       402

    accuracy                           0.60       804
   macro avg       0.60      0.60      0.60       804
weighted avg       0.60      0.60      0.60       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.63      0.63       402
           1       0.62      0.61      0.62       402

    accuracy                           0.62       804
   macro avg       0.62      0.62      0.62       804
weighted avg       0.62      0.62      0.62       804

Tested: n_clusters=7, k_neighbours=9, metric=<function cityblock at 0x123913310> --> Acc: 0.6440
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.60      0.66       402
           1       0.66      0.79      0.72       402

    accuracy                           0.69       804
   macro avg       0.70      0.69      0.69       804
weighted avg       0.70      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.67      0.69       402
           1       0.69      0.72      0.70       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.70      0.73       402
           1       0.72      0.77      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.60      0.63       402
           1       0.63      0.69      0.66       402

    accuracy                           0.64       804
   macro avg       0.65      0.64      0.64       804
weighted avg       0.65      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.60      0.67       402
           1       0.67      0.81      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Tested: n_clusters=7, k_neighbours=9, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6950
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.55      0.61       402
           1       0.62      0.74      0.68       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.64       804
weighted avg       0.65      0.65      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.72      0.72       402
           1       0.72      0.72      0.72       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.70      0.73       402
           1       0.72      0.78      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.71      0.68       402
           1       0.69      0.64      0.66       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.72      0.69       402
           1       0.69      0.64      0.67       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Tested: n_clusters=7, k_neighbours=11, metric=<function euclidean at 0x123901e50> --> Acc: 0.6925
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.53      0.57       402
           1       0.59      0.67      0.63       402

    accuracy                           0.60       804
   macro avg       0.61      0.60      0.60       804
weighted avg       0.61      0.60      0.60       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.69      0.66       402
           1       0.66      0.60      0.63       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.68      0.65       402
           1       0.65      0.58      0.61       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.56      0.63       402
           1       0.64      0.78      0.70       402

    accuracy                           0.67       804
   macro avg       0.68      0.67      0.67       804
weighted avg       0.68      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.66      0.65       402
           1       0.65      0.62      0.64       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Tested: n_clusters=7, k_neighbours=11, metric=<function cityblock at 0x123913310> --> Acc: 0.6391
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.42      0.54       402
           1       0.60      0.87      0.71       402

    accuracy                           0.65       804
   macro avg       0.68      0.65      0.63       804
weighted avg       0.68      0.65      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.63      0.67       402
           1       0.67      0.77      0.72       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.67      0.65       402
           1       0.65      0.60      0.62       402

    accuracy                           0.63       804
   macro avg       0.64      0.63      0.63       804
weighted avg       0.64      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.66      0.67       402
           1       0.67      0.70      0.69       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.55      0.60       402
           1       0.61      0.71      0.66       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Tested: n_clusters=7, k_neighbours=11, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6572
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.57      0.61       402
           1       0.62      0.69      0.65       402

    accuracy                           0.63       804
   macro avg       0.64      0.63      0.63       804
weighted avg       0.64      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.55      0.83      0.66       402
           1       0.65      0.32      0.43       402

    accuracy                           0.57       804
   macro avg       0.60      0.57      0.54       804
weighted avg       0.60      0.57      0.54       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.59      0.60      0.60       402
           1       0.60      0.59      0.59       402

    accuracy                           0.59       804
   macro avg       0.59      0.59      0.59       804
weighted avg       0.59      0.59      0.59       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.56      0.85      0.68       402
           1       0.69      0.33      0.45       402

    accuracy                           0.59       804
   macro avg       0.63      0.59      0.56       804
weighted avg       0.63      0.59      0.56       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.42      0.51       402
           1       0.57      0.77      0.66       402

    accuracy                           0.60       804
   macro avg       0.61      0.60      0.58       804
weighted avg       0.61      0.60      0.58       804

Tested: n_clusters=7, k_neighbours=13, metric=<function euclidean at 0x123901e50> --> Acc: 0.5985
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.69      0.65       402
           1       0.65      0.56      0.60       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.61      0.63       402
           1       0.63      0.65      0.64       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.54      0.67      0.60       402
           1       0.57      0.43      0.49       402

    accuracy                           0.55       804
   macro avg       0.55      0.55      0.54       804
weighted avg       0.55      0.55      0.54       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.34      0.44       402
           1       0.55      0.80      0.65       402

    accuracy                           0.57       804
   macro avg       0.59      0.57      0.55       804
weighted avg       0.59      0.57      0.55       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.55      0.60       402
           1       0.61      0.71      0.66       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Tested: n_clusters=7, k_neighbours=13, metric=<function cityblock at 0x123913310> --> Acc: 0.6025
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.56      0.76      0.64       402
           1       0.62      0.40      0.48       402

    accuracy                           0.58       804
   macro avg       0.59      0.58      0.56       804
weighted avg       0.59      0.58      0.56       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.56      0.68      0.62       402
           1       0.60      0.48      0.53       402

    accuracy                           0.58       804
   macro avg       0.58      0.58      0.57       804
weighted avg       0.58      0.58      0.57       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.68      0.65       402
           1       0.65      0.61      0.63       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.66      0.64       402
           1       0.64      0.60      0.62       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.56      0.60       402
           1       0.61      0.69      0.64       402

    accuracy                           0.62       804
   macro avg       0.62      0.62      0.62       804
weighted avg       0.62      0.62      0.62       804

Tested: n_clusters=7, k_neighbours=13, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6100
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.67      0.70       402
           1       0.70      0.77      0.73       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.69      0.71       402
           1       0.70      0.73      0.72       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.60      0.67       402
           1       0.67      0.80      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.72      0.69       402
           1       0.70      0.64      0.67       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.66      0.70       402
           1       0.70      0.78      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Tested: n_clusters=8, k_neighbours=3, metric=<function euclidean at 0x123901e50> --> Acc: 0.7062
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.51      0.61       402
           1       0.63      0.81      0.71       402

    accuracy                           0.66       804
   macro avg       0.68      0.66      0.66       804
weighted avg       0.68      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.64      0.68       402
           1       0.68      0.75      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.63      0.65       402
           1       0.65      0.68      0.66       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.73      0.68       402
           1       0.68      0.58      0.63       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.65       804
weighted avg       0.66      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.62      0.67       402
           1       0.67      0.77      0.72       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.69       804
weighted avg       0.70      0.70      0.69       804

Tested: n_clusters=8, k_neighbours=3, metric=<function cityblock at 0x123913310> --> Acc: 0.6739
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.67      0.70       402
           1       0.70      0.75      0.72       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.77      0.76       402
           1       0.76      0.74      0.75       402

    accuracy                           0.76       804
   macro avg       0.76      0.76      0.76       804
weighted avg       0.76      0.76      0.76       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.77      0.76       402
           1       0.76      0.74      0.75       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.65      0.70       402
           1       0.69      0.79      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.69      0.70       402
           1       0.70      0.72      0.71       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Tested: n_clusters=8, k_neighbours=3, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.7294
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.74      0.73       402
           1       0.73      0.71      0.72       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.67      0.71       402
           1       0.70      0.78      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.72       804
weighted avg       0.73      0.73      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.66      0.69       402
           1       0.69      0.74      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.58      0.64       402
           1       0.65      0.78      0.71       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.68       804
weighted avg       0.69      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.73      0.70       402
           1       0.71      0.65      0.68       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Tested: n_clusters=8, k_neighbours=5, metric=<function euclidean at 0x123901e50> --> Acc: 0.7045
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.70      0.70       402
           1       0.70      0.69      0.70       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.40      0.52       402
           1       0.59      0.87      0.70       402

    accuracy                           0.63       804
   macro avg       0.67      0.63      0.61       804
weighted avg       0.67      0.63      0.61       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.57      0.63       402
           1       0.64      0.77      0.70       402

    accuracy                           0.67       804
   macro avg       0.68      0.67      0.67       804
weighted avg       0.68      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.75      0.69       402
           1       0.70      0.58      0.64       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.66       804
weighted avg       0.67      0.67      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.58      0.63       402
           1       0.64      0.74      0.69       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.66       804
weighted avg       0.67      0.66      0.66       804

Tested: n_clusters=8, k_neighbours=5, metric=<function cityblock at 0x123913310> --> Acc: 0.6654
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.70      0.72       402
           1       0.72      0.76      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.80      0.74       402
           1       0.76      0.63      0.69       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.76      0.72       402
           1       0.73      0.65      0.69       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.67      0.69       402
           1       0.69      0.73      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.67      0.69       402
           1       0.69      0.71      0.70       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Tested: n_clusters=8, k_neighbours=5, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.7085
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.63      0.68       402
           1       0.68      0.80      0.73       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.74      0.73       402
           1       0.73      0.72      0.73       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.66      0.68       402
           1       0.67      0.71      0.69       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.78      0.70       402
           1       0.71      0.55      0.62       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.66       804
weighted avg       0.67      0.67      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.64      0.68       402
           1       0.68      0.75      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.69       804
weighted avg       0.70      0.70      0.69       804

Tested: n_clusters=8, k_neighbours=7, metric=<function euclidean at 0x123901e50> --> Acc: 0.6975
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.72      0.68       402
           1       0.68      0.58      0.62       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.81      0.71       402
           1       0.73      0.51      0.60       402

    accuracy                           0.66       804
   macro avg       0.68      0.66      0.65       804
weighted avg       0.68      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.66      0.67       402
           1       0.67      0.69      0.68       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.45      0.55       402
           1       0.60      0.82      0.69       402

    accuracy                           0.63       804
   macro avg       0.66      0.63      0.62       804
weighted avg       0.66      0.63      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.71      0.68       402
           1       0.69      0.62      0.65       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Tested: n_clusters=8, k_neighbours=7, metric=<function cityblock at 0x123913310> --> Acc: 0.6582
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.65      0.68       402
           1       0.68      0.74      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.69       804
weighted avg       0.70      0.70      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.66      0.70       402
           1       0.69      0.77      0.73       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.66      0.69       402
           1       0.69      0.75      0.72       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.70       804
weighted avg       0.71      0.71      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.47      0.58       402
           1       0.61      0.85      0.71       402

    accuracy                           0.66       804
   macro avg       0.68      0.66      0.64       804
weighted avg       0.68      0.66      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.77      0.70       402
           1       0.71      0.56      0.62       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.66       804
weighted avg       0.67      0.67      0.66       804

Tested: n_clusters=8, k_neighbours=7, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6878
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.68      0.67       402
           1       0.67      0.65      0.66       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.71      0.71       402
           1       0.71      0.71      0.71       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.62      0.68       402
           1       0.68      0.79      0.73       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.70       804
weighted avg       0.71      0.71      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.64      0.66       402
           1       0.66      0.71      0.69       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.77      0.46      0.57       402
           1       0.61      0.87      0.72       402

    accuracy                           0.66       804
   macro avg       0.69      0.66      0.65       804
weighted avg       0.69      0.66      0.65       804

Tested: n_clusters=8, k_neighbours=9, metric=<function euclidean at 0x123901e50> --> Acc: 0.6821
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.65      0.68       402
           1       0.68      0.76      0.72       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.65      0.65       402
           1       0.65      0.63      0.64       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.61      0.64       402
           1       0.64      0.69      0.66       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.56      0.62       402
           1       0.63      0.77      0.69       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.66       804
weighted avg       0.67      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.62      0.65       402
           1       0.66      0.72      0.69       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Tested: n_clusters=8, k_neighbours=9, metric=<function cityblock at 0x123913310> --> Acc: 0.6659
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.78      0.55      0.65       402
           1       0.65      0.84      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.69       804
weighted avg       0.71      0.70      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.59      0.66       402
           1       0.66      0.80      0.72       402

    accuracy                           0.69       804
   macro avg       0.70      0.69      0.69       804
weighted avg       0.70      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.72      0.72       402
           1       0.72      0.72      0.72       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.56      0.63       402
           1       0.64      0.78      0.70       402

    accuracy                           0.67       804
   macro avg       0.68      0.67      0.67       804
weighted avg       0.68      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.59      0.62       402
           1       0.63      0.69      0.66       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Tested: n_clusters=8, k_neighbours=9, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6833
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.52      0.62       402
           1       0.63      0.82      0.72       402

    accuracy                           0.67       804
   macro avg       0.69      0.67      0.67       804
weighted avg       0.69      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.59      0.67       402
           1       0.67      0.81      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.74      0.71       402
           1       0.71      0.66      0.69       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.66      0.69       402
           1       0.68      0.73      0.70       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.69       804
weighted avg       0.70      0.70      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.61      0.64       402
           1       0.64      0.70      0.67       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Tested: n_clusters=8, k_neighbours=11, metric=<function euclidean at 0x123901e50> --> Acc: 0.6843
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.57      0.64       402
           1       0.65      0.78      0.71       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.67       804
weighted avg       0.69      0.68      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.63      0.65       402
           1       0.65      0.68      0.67       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.53      0.59       402
           1       0.61      0.73      0.66       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.54      0.60       402
           1       0.62      0.74      0.67       402

    accuracy                           0.64       804
   macro avg       0.65      0.64      0.64       804
weighted avg       0.65      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.63      0.66       402
           1       0.66      0.72      0.69       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Tested: n_clusters=8, k_neighbours=11, metric=<function cityblock at 0x123913310> --> Acc: 0.6560
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.65      0.68       402
           1       0.68      0.74      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.70      0.69       402
           1       0.69      0.67      0.68       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.76      0.73       402
           1       0.74      0.69      0.72       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.78      0.72       402
           1       0.73      0.61      0.67       402

    accuracy                           0.69       804
   macro avg       0.70      0.69      0.69       804
weighted avg       0.70      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.52      0.60       402
           1       0.62      0.78      0.69       402

    accuracy                           0.65       804
   macro avg       0.66      0.65      0.64       804
weighted avg       0.66      0.65      0.64       804

Tested: n_clusters=8, k_neighbours=11, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6913
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.82      0.70       402
           1       0.72      0.47      0.57       402

    accuracy                           0.64       804
   macro avg       0.66      0.64      0.63       804
weighted avg       0.66      0.64      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.81      0.70       402
           1       0.73      0.50      0.59       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.65       804
weighted avg       0.67      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.84      0.72       402
           1       0.76      0.52      0.62       402

    accuracy                           0.68       804
   macro avg       0.70      0.68      0.67       804
weighted avg       0.70      0.68      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.69      0.70       402
           1       0.70      0.73      0.71       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.56      0.82      0.67       402
           1       0.67      0.36      0.47       402

    accuracy                           0.59       804
   macro avg       0.62      0.59      0.57       804
weighted avg       0.62      0.59      0.57       804

Tested: n_clusters=8, k_neighbours=13, metric=<function euclidean at 0x123901e50> --> Acc: 0.6555
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.68      0.66       402
           1       0.66      0.61      0.63       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.58      0.86      0.69       402
           1       0.73      0.39      0.51       402

    accuracy                           0.62       804
   macro avg       0.66      0.62      0.60       804
weighted avg       0.66      0.62      0.60       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.51      0.59       402
           1       0.62      0.79      0.69       402

    accuracy                           0.65       804
   macro avg       0.66      0.65      0.64       804
weighted avg       0.66      0.65      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.65      0.64       402
           1       0.63      0.60      0.62       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.43      0.54       402
           1       0.59      0.83      0.69       402

    accuracy                           0.63       804
   macro avg       0.65      0.63      0.61       804
weighted avg       0.65      0.63      0.61       804

Tested: n_clusters=8, k_neighbours=13, metric=<function cityblock at 0x123913310> --> Acc: 0.6348
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.58      0.66       402
           1       0.66      0.81      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.69       804
weighted avg       0.71      0.70      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.82      0.70       402
           1       0.73      0.48      0.58       402

    accuracy                           0.65       804
   macro avg       0.67      0.65      0.64       804
weighted avg       0.67      0.65      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.65      0.66       402
           1       0.66      0.68      0.67       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.56      0.64       402
           1       0.65      0.82      0.73       402

    accuracy                           0.69       804
   macro avg       0.70      0.69      0.68       804
weighted avg       0.70      0.69      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.81      0.69       402
           1       0.71      0.48      0.57       402

    accuracy                           0.64       804
   macro avg       0.66      0.64      0.63       804
weighted avg       0.66      0.64      0.63       804

Tested: n_clusters=8, k_neighbours=13, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6684
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.55      0.89      0.68       402
           1       0.71      0.27      0.39       402

    accuracy                           0.58       804
   macro avg       0.63      0.58      0.53       804
weighted avg       0.63      0.58      0.53       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.74      0.68       402
           1       0.68      0.55      0.61       402

    accuracy                           0.64       804
   macro avg       0.65      0.64      0.64       804
weighted avg       0.65      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.45      0.55       402
           1       0.59      0.80      0.68       402

    accuracy                           0.63       804
   macro avg       0.64      0.63      0.61       804
weighted avg       0.64      0.63      0.61       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.63      0.64       402
           1       0.64      0.65      0.65       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.55      0.80      0.65       402
           1       0.64      0.34      0.45       402

    accuracy                           0.57       804
   macro avg       0.59      0.57      0.55       804
weighted avg       0.59      0.57      0.55       804

Tested: n_clusters=8, k_neighbours=15, metric=<function euclidean at 0x123901e50> --> Acc: 0.6129
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.57      0.76      0.65       402
           1       0.63      0.42      0.51       402

    accuracy                           0.59       804
   macro avg       0.60      0.59      0.58       804
weighted avg       0.60      0.59      0.58       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.51      0.75      0.61       402
           1       0.53      0.28      0.36       402

    accuracy                           0.51       804
   macro avg       0.52      0.51      0.49       804
weighted avg       0.52      0.51      0.49       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.50      0.99      0.66       402
           1       0.25      0.00      0.00       402

    accuracy                           0.50       804
   macro avg       0.37      0.50      0.33       804
weighted avg       0.37      0.50      0.33       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.39      0.50       402
           1       0.57      0.81      0.67       402

    accuracy                           0.60       804
   macro avg       0.62      0.60      0.58       804
weighted avg       0.62      0.60      0.58       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.30      0.42       402
           1       0.56      0.89      0.69       402

    accuracy                           0.59       804
   macro avg       0.64      0.59      0.55       804
weighted avg       0.64      0.59      0.55       804

Tested: n_clusters=8, k_neighbours=15, metric=<function cityblock at 0x123913310> --> Acc: 0.5587
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.63      0.66       402
           1       0.66      0.70      0.68       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.51      0.57       402
           1       0.60      0.74      0.66       402

    accuracy                           0.62       804
   macro avg       0.63      0.62      0.62       804
weighted avg       0.63      0.62      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.67      0.66       402
           1       0.66      0.65      0.66       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.73      0.68       402
           1       0.68      0.58      0.62       402

    accuracy                           0.65       804
   macro avg       0.66      0.65      0.65       804
weighted avg       0.66      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.56      0.84      0.67       402
           1       0.67      0.33      0.44       402

    accuracy                           0.58       804
   macro avg       0.61      0.58      0.56       804
weighted avg       0.61      0.58      0.56       804

Tested: n_clusters=8, k_neighbours=15, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6376
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.69      0.71       402
           1       0.71      0.75      0.73       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.77      0.73      0.75       402
           1       0.74      0.78      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.62      0.68       402
           1       0.68      0.80      0.73       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.77      0.61      0.68       402
           1       0.68      0.82      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.71       804
weighted avg       0.72      0.72      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.77      0.68      0.72       402
           1       0.71      0.80      0.75       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Tested: n_clusters=9, k_neighbours=3, metric=<function euclidean at 0x123901e50> --> Acc: 0.7274
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.68      0.69       402
           1       0.69      0.69      0.69       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.72      0.69       402
           1       0.70      0.63      0.66       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.58      0.64       402
           1       0.64      0.75      0.69       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.66       804
weighted avg       0.67      0.67      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.73      0.68       402
           1       0.69      0.59      0.64       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.66       804
weighted avg       0.67      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.60      0.64       402
           1       0.65      0.73      0.68       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.66       804
weighted avg       0.67      0.66      0.66       804

Tested: n_clusters=9, k_neighbours=3, metric=<function cityblock at 0x123913310> --> Acc: 0.6721
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.72      0.73       402
           1       0.73      0.75      0.74       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.73      0.74       402
           1       0.74      0.74      0.74       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.72      0.72       402
           1       0.72      0.73      0.73       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.66      0.70       402
           1       0.70      0.77      0.73       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.71       804
weighted avg       0.72      0.72      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.72      0.71       402
           1       0.71      0.70      0.70       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Tested: n_clusters=9, k_neighbours=3, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.7241
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.73      0.72       402
           1       0.72      0.71      0.72       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.79      0.74       402
           1       0.75      0.65      0.70       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.71      0.69       402
           1       0.69      0.65      0.67       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.63      0.68       402
           1       0.68      0.76      0.72       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.66      0.69       402
           1       0.69      0.74      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Tested: n_clusters=9, k_neighbours=5, metric=<function euclidean at 0x123901e50> --> Acc: 0.7040
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.58      0.65       402
           1       0.66      0.80      0.72       402

    accuracy                           0.69       804
   macro avg       0.70      0.69      0.69       804
weighted avg       0.70      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.71      0.71       402
           1       0.71      0.69      0.70       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.75      0.71       402
           1       0.72      0.64      0.68       402

    accuracy                           0.69       804
   macro avg       0.70      0.69      0.69       804
weighted avg       0.70      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.63      0.66       402
           1       0.66      0.71      0.68       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.69      0.68       402
           1       0.68      0.64      0.66       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Tested: n_clusters=9, k_neighbours=5, metric=<function cityblock at 0x123913310> --> Acc: 0.6848
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.78      0.72       402
           1       0.74      0.62      0.68       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.71      0.72       402
           1       0.72      0.75      0.73       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.66      0.70       402
           1       0.69      0.77      0.73       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.73      0.71       402
           1       0.72      0.69      0.70       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.64      0.67       402
           1       0.67      0.72      0.69       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Tested: n_clusters=9, k_neighbours=5, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.7060
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.66      0.69       402
           1       0.69      0.75      0.72       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.75      0.73       402
           1       0.74      0.70      0.72       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.78      0.74       402
           1       0.76      0.68      0.71       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.57      0.64       402
           1       0.65      0.78      0.71       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.67       804
weighted avg       0.68      0.68      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.72      0.70       402
           1       0.71      0.67      0.69       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Tested: n_clusters=9, k_neighbours=7, metric=<function euclidean at 0x123901e50> --> Acc: 0.7060
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.69      0.68       402
           1       0.68      0.65      0.66       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.59      0.61       402
           1       0.62      0.66      0.64       402

    accuracy                           0.62       804
   macro avg       0.62      0.62      0.62       804
weighted avg       0.62      0.62      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.54      0.61       402
           1       0.63      0.77      0.69       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.65       804
weighted avg       0.67      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.62      0.65       402
           1       0.65      0.71      0.68       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.36      0.49       402
           1       0.58      0.89      0.70       402

    accuracy                           0.63       804
   macro avg       0.67      0.63      0.60       804
weighted avg       0.67      0.63      0.60       804

Tested: n_clusters=9, k_neighbours=7, metric=<function cityblock at 0x123913310> --> Acc: 0.6488
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.66      0.70       402
           1       0.70      0.78      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.77      0.73       402
           1       0.75      0.68      0.71       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.72      0.73       402
           1       0.73      0.75      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.82      0.52      0.63       402
           1       0.65      0.89      0.75       402

    accuracy                           0.70       804
   macro avg       0.73      0.70      0.69       804
weighted avg       0.73      0.70      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.67      0.71       402
           1       0.71      0.79      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Tested: n_clusters=9, k_neighbours=7, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.7216
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.77      0.52      0.62       402
           1       0.64      0.84      0.73       402

    accuracy                           0.68       804
   macro avg       0.70      0.68      0.67       804
weighted avg       0.70      0.68      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.80      0.74       402
           1       0.76      0.63      0.69       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.65      0.68       402
           1       0.68      0.74      0.71       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.65      0.68       402
           1       0.68      0.73      0.70       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.69      0.69       402
           1       0.69      0.69      0.69       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Tested: n_clusters=9, k_neighbours=9, metric=<function euclidean at 0x123901e50> --> Acc: 0.6943
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.54      0.61       402
           1       0.63      0.78      0.70       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.65       804
weighted avg       0.67      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.52      0.60       402
           1       0.62      0.79      0.70       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.65       804
weighted avg       0.67      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.80      0.71       402
           1       0.73      0.54      0.62       402

    accuracy                           0.67       804
   macro avg       0.68      0.67      0.66       804
weighted avg       0.68      0.67      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.53      0.61       402
           1       0.63      0.79      0.70       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.65       804
weighted avg       0.67      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.58      0.64       402
           1       0.64      0.75      0.69       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.66       804
weighted avg       0.67      0.67      0.66       804

Tested: n_clusters=9, k_neighbours=9, metric=<function cityblock at 0x123913310> --> Acc: 0.6622
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.72      0.73       402
           1       0.73      0.75      0.74       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.78      0.71       402
           1       0.73      0.58      0.65       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.68       804
weighted avg       0.69      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.66      0.67       402
           1       0.67      0.70      0.69       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.68      0.69       402
           1       0.69      0.71      0.70       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.62      0.65       402
           1       0.65      0.71      0.68       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.66       804
weighted avg       0.67      0.67      0.66       804

Tested: n_clusters=9, k_neighbours=9, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6923
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.79      0.53      0.64       402
           1       0.65      0.86      0.74       402

    accuracy                           0.70       804
   macro avg       0.72      0.70      0.69       804
weighted avg       0.72      0.70      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.73      0.72       402
           1       0.72      0.70      0.71       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.76      0.71       402
           1       0.72      0.63      0.68       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.69      0.69       402
           1       0.69      0.69      0.69       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.55      0.63       402
           1       0.64      0.81      0.71       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.67       804
weighted avg       0.69      0.68      0.67       804

Tested: n_clusters=9, k_neighbours=11, metric=<function euclidean at 0x123901e50> --> Acc: 0.6950
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.58      0.64       402
           1       0.65      0.77      0.70       402

    accuracy                           0.67       804
   macro avg       0.68      0.67      0.67       804
weighted avg       0.68      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.77      0.50      0.60       402
           1       0.63      0.85      0.72       402

    accuracy                           0.67       804
   macro avg       0.70      0.67      0.66       804
weighted avg       0.70      0.67      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.62      0.65       402
           1       0.65      0.71      0.68       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.66       804
weighted avg       0.67      0.67      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.69      0.68       402
           1       0.68      0.65      0.66       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.60      0.64       402
           1       0.64      0.71      0.67       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Tested: n_clusters=9, k_neighbours=11, metric=<function cityblock at 0x123913310> --> Acc: 0.6674
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.83      0.42      0.56       402
           1       0.61      0.91      0.73       402

    accuracy                           0.67       804
   macro avg       0.72      0.67      0.65       804
weighted avg       0.72      0.67      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.71      0.72       402
           1       0.71      0.73      0.72       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.68      0.70       402
           1       0.70      0.74      0.72       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.66      0.69       402
           1       0.69      0.75      0.72       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.70      0.71       402
           1       0.71      0.73      0.72       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Tested: n_clusters=9, k_neighbours=11, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.7020
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.57      0.64       402
           1       0.65      0.80      0.72       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.68       804
weighted avg       0.69      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.59      0.82      0.69       402
           1       0.70      0.44      0.54       402

    accuracy                           0.63       804
   macro avg       0.65      0.63      0.61       804
weighted avg       0.65      0.63      0.61       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.65      0.70       402
           1       0.69      0.79      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.70      0.67       402
           1       0.67      0.61      0.64       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.55      0.63       402
           1       0.64      0.79      0.71       402

    accuracy                           0.67       804
   macro avg       0.68      0.67      0.67       804
weighted avg       0.68      0.67      0.67       804

Tested: n_clusters=9, k_neighbours=13, metric=<function euclidean at 0x123901e50> --> Acc: 0.6704
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.70      0.69       402
           1       0.69      0.66      0.67       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.73      0.68       402
           1       0.68      0.58      0.63       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.65       804
weighted avg       0.66      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.50      0.57       402
           1       0.60      0.75      0.67       402

    accuracy                           0.62       804
   macro avg       0.63      0.62      0.62       804
weighted avg       0.63      0.62      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.66      0.65       402
           1       0.65      0.64      0.65       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.46      0.56       402
           1       0.60      0.81      0.69       402

    accuracy                           0.63       804
   macro avg       0.65      0.63      0.62       804
weighted avg       0.65      0.63      0.62       804

Tested: n_clusters=9, k_neighbours=13, metric=<function cityblock at 0x123913310> --> Acc: 0.6485
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.38      0.50       402
           1       0.58      0.88      0.70       402

    accuracy                           0.63       804
   macro avg       0.67      0.63      0.60       804
weighted avg       0.67      0.63      0.60       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.78      0.71       402
           1       0.73      0.57      0.64       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.68       804
weighted avg       0.69      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.67      0.67       402
           1       0.67      0.66      0.67       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.68      0.68       402
           1       0.68      0.66      0.67       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.58      0.82      0.68       402
           1       0.69      0.41      0.51       402

    accuracy                           0.61       804
   macro avg       0.63      0.61      0.59       804
weighted avg       0.63      0.61      0.59       804

Tested: n_clusters=9, k_neighbours=13, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6515
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.67      0.67       402
           1       0.67      0.67      0.67       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.81      0.72       402
           1       0.74      0.55      0.63       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.67       804
weighted avg       0.69      0.68      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.80      0.71       402
           1       0.74      0.55      0.63       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.67       804
weighted avg       0.69      0.68      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.63      0.62       402
           1       0.62      0.61      0.61       402

    accuracy                           0.62       804
   macro avg       0.62      0.62      0.62       804
weighted avg       0.62      0.62      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.53      0.62       402
           1       0.64      0.84      0.72       402

    accuracy                           0.68       804
   macro avg       0.70      0.68      0.67       804
weighted avg       0.70      0.68      0.67       804

Tested: n_clusters=9, k_neighbours=15, metric=<function euclidean at 0x123901e50> --> Acc: 0.6657
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.54      0.61       402
           1       0.63      0.78      0.70       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.65       804
weighted avg       0.67      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.73      0.67       402
           1       0.67      0.54      0.60       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.63       804
weighted avg       0.64      0.64      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.59      0.62       402
           1       0.63      0.69      0.65       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.70      0.65       402
           1       0.65      0.55      0.59       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.62       804
weighted avg       0.63      0.63      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.45      0.54       402
           1       0.59      0.79      0.67       402

    accuracy                           0.62       804
   macro avg       0.63      0.62      0.60       804
weighted avg       0.63      0.62      0.60       804

Tested: n_clusters=9, k_neighbours=15, metric=<function cityblock at 0x123913310> --> Acc: 0.6351
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.62      0.65       402
           1       0.66      0.72      0.69       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.60      0.63       402
           1       0.63      0.69      0.66       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.53      0.63       402
           1       0.64      0.84      0.73       402

    accuracy                           0.68       804
   macro avg       0.70      0.68      0.68       804
weighted avg       0.70      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.76      0.70       402
           1       0.71      0.60      0.65       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.68      0.65       402
           1       0.65      0.58      0.61       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Tested: n_clusters=9, k_neighbours=15, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6627
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.50      0.58       402
           1       0.61      0.79      0.69       402

    accuracy                           0.64       804
   macro avg       0.66      0.64      0.64       804
weighted avg       0.66      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.64      0.64       402
           1       0.63      0.62      0.63       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.57      0.89      0.70       402
           1       0.76      0.34      0.47       402

    accuracy                           0.62       804
   macro avg       0.67      0.62      0.58       804
weighted avg       0.67      0.62      0.58       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.59      0.80      0.68       402
           1       0.69      0.45      0.54       402

    accuracy                           0.62       804
   macro avg       0.64      0.62      0.61       804
weighted avg       0.64      0.62      0.61       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.55      0.75      0.63       402
           1       0.60      0.38      0.47       402

    accuracy                           0.56       804
   macro avg       0.57      0.56      0.55       804
weighted avg       0.57      0.56      0.55       804

Tested: n_clusters=9, k_neighbours=17, metric=<function euclidean at 0x123901e50> --> Acc: 0.6159
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.50      0.99      0.66       402
           1       0.00      0.00      0.00       402

    accuracy                           0.50       804
   macro avg       0.25      0.50      0.33       804
weighted avg       0.25      0.50      0.33       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.57      0.79      0.66       402
           1       0.66      0.41      0.50       402

    accuracy                           0.60       804
   macro avg       0.61      0.60      0.58       804
weighted avg       0.61      0.60      0.58       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.59      0.47      0.53       402
           1       0.56      0.67      0.61       402

    accuracy                           0.57       804
   macro avg       0.58      0.57      0.57       804
weighted avg       0.58      0.57      0.57       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.50      0.88      0.64       402
           1       0.52      0.13      0.20       402

    accuracy                           0.50       804
   macro avg       0.51      0.50      0.42       804
weighted avg       0.51      0.50      0.42       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.58      0.63      0.60       402
           1       0.59      0.53      0.56       402

    accuracy                           0.58       804
   macro avg       0.59      0.58      0.58       804
weighted avg       0.59      0.58      0.58       804

Tested: n_clusters=9, k_neighbours=17, metric=<function cityblock at 0x123913310> --> Acc: 0.5510
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.45      0.52       402
           1       0.56      0.71      0.63       402

    accuracy                           0.58       804
   macro avg       0.59      0.58      0.57       804
weighted avg       0.59      0.58      0.57       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.70      0.67       402
           1       0.67      0.61      0.64       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.46      0.53       402
           1       0.58      0.73      0.64       402

    accuracy                           0.60       804
   macro avg       0.60      0.60      0.59       804
weighted avg       0.60      0.60      0.59       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.54      0.85      0.66       402
           1       0.66      0.28      0.39       402

    accuracy                           0.57       804
   macro avg       0.60      0.57      0.53       804
weighted avg       0.60      0.57      0.53       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.55      0.82      0.66       402
           1       0.66      0.34      0.44       402

    accuracy                           0.58       804
   macro avg       0.60      0.58      0.55       804
weighted avg       0.60      0.58      0.55       804

Tested: n_clusters=9, k_neighbours=17, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.5960
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.75      0.73       402
           1       0.73      0.68      0.70       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.71       804
weighted avg       0.72      0.72      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.72      0.73       402
           1       0.73      0.74      0.73       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.77      0.74       402
           1       0.75      0.69      0.72       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.67      0.69       402
           1       0.69      0.73      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.70      0.72       402
           1       0.72      0.76      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Tested: n_clusters=10, k_neighbours=3, metric=<function euclidean at 0x123901e50> --> Acc: 0.7199
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.49      0.58       402
           1       0.61      0.82      0.70       402

    accuracy                           0.65       804
   macro avg       0.67      0.65      0.64       804
weighted avg       0.67      0.65      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.75      0.72       402
           1       0.72      0.66      0.69       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.65      0.68       402
           1       0.68      0.76      0.72       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.67      0.67       402
           1       0.67      0.67      0.67       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.64      0.67       402
           1       0.67      0.73      0.70       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.68       804
weighted avg       0.69      0.69      0.68       804

Tested: n_clusters=10, k_neighbours=3, metric=<function cityblock at 0x123913310> --> Acc: 0.6828
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.71      0.72       402
           1       0.72      0.75      0.73       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.76      0.75       402
           1       0.75      0.72      0.73       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.74       804
weighted avg       0.74      0.74      0.74       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.66      0.69       402
           1       0.69      0.77      0.73       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.66      0.69       402
           1       0.69      0.74      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.59      0.65       402
           1       0.66      0.78      0.71       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.68       804
weighted avg       0.69      0.69      0.68       804

Tested: n_clusters=10, k_neighbours=3, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.7134
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.66      0.70       402
           1       0.70      0.77      0.73       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.76      0.75       402
           1       0.75      0.73      0.74       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.74       804
weighted avg       0.75      0.75      0.74       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.78      0.63      0.70       402
           1       0.69      0.82      0.75       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.72       804
weighted avg       0.74      0.73      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.67      0.69       402
           1       0.69      0.72      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.77      0.52      0.62       402
           1       0.64      0.84      0.73       402

    accuracy                           0.68       804
   macro avg       0.70      0.68      0.67       804
weighted avg       0.70      0.68      0.67       804

Tested: n_clusters=10, k_neighbours=5, metric=<function euclidean at 0x123901e50> --> Acc: 0.7137
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.58      0.64       402
           1       0.65      0.77      0.70       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.67       804
weighted avg       0.68      0.68      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.73      0.69       402
           1       0.70      0.61      0.65       402

    accuracy                           0.67       804
   macro avg       0.68      0.67      0.67       804
weighted avg       0.68      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.76      0.70       402
           1       0.71      0.59      0.65       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.50      0.58       402
           1       0.61      0.79      0.69       402

    accuracy                           0.64       804
   macro avg       0.65      0.64      0.63       804
weighted avg       0.65      0.64      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.61      0.64       402
           1       0.65      0.71      0.68       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Tested: n_clusters=10, k_neighbours=5, metric=<function cityblock at 0x123913310> --> Acc: 0.6662
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.72      0.71       402
           1       0.71      0.69      0.70       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.83      0.60      0.69       402
           1       0.69      0.87      0.77       402

    accuracy                           0.74       804
   macro avg       0.76      0.74      0.73       804
weighted avg       0.76      0.74      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.70      0.72       402
           1       0.72      0.77      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.67      0.69       402
           1       0.69      0.74      0.71       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.72      0.70       402
           1       0.70      0.67      0.69       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Tested: n_clusters=10, k_neighbours=5, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.7144
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.72      0.73       402
           1       0.73      0.74      0.73       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.68      0.71       402
           1       0.71      0.77      0.74       402

    accuracy                           0.73       804
   macro avg       0.73      0.73      0.73       804
weighted avg       0.73      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.80      0.59      0.68       402
           1       0.68      0.86      0.76       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.72       804
weighted avg       0.74      0.73      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.66      0.69       402
           1       0.69      0.74      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.63      0.67       402
           1       0.67      0.74      0.70       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Tested: n_clusters=10, k_neighbours=7, metric=<function euclidean at 0x123901e50> --> Acc: 0.7137
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.54      0.62       402
           1       0.63      0.78      0.70       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.66       804
weighted avg       0.67      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.69      0.68       402
           1       0.68      0.66      0.67       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.66      0.68       402
           1       0.68      0.72      0.70       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.63      0.66       402
           1       0.66      0.72      0.69       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.67       804
weighted avg       0.68      0.68      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.67      0.67       402
           1       0.67      0.66      0.66       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Tested: n_clusters=10, k_neighbours=7, metric=<function cityblock at 0x123913310> --> Acc: 0.6734
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.81      0.55      0.65       402
           1       0.66      0.87      0.75       402

    accuracy                           0.71       804
   macro avg       0.73      0.71      0.70       804
weighted avg       0.73      0.71      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.62      0.68       402
           1       0.68      0.79      0.73       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.70       804
weighted avg       0.71      0.71      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.78      0.69      0.73       402
           1       0.72      0.81      0.76       402

    accuracy                           0.75       804
   macro avg       0.75      0.75      0.75       804
weighted avg       0.75      0.75      0.75       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.84      0.72       402
           1       0.77      0.52      0.62       402

    accuracy                           0.68       804
   macro avg       0.70      0.68      0.67       804
weighted avg       0.70      0.68      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.78      0.52      0.63       402
           1       0.64      0.85      0.73       402

    accuracy                           0.69       804
   macro avg       0.71      0.69      0.68       804
weighted avg       0.71      0.69      0.68       804

Tested: n_clusters=10, k_neighbours=7, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.7065
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.64      0.69       402
           1       0.69      0.80      0.74       402

    accuracy                           0.72       804
   macro avg       0.72      0.72      0.72       804
weighted avg       0.72      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.79      0.55      0.65       402
           1       0.65      0.85      0.74       402

    accuracy                           0.70       804
   macro avg       0.72      0.70      0.69       804
weighted avg       0.72      0.70      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.82      0.75       402
           1       0.78      0.65      0.71       402

    accuracy                           0.73       804
   macro avg       0.74      0.73      0.73       804
weighted avg       0.74      0.73      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.59      0.66       402
           1       0.66      0.80      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.69       804
weighted avg       0.71      0.70      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.77      0.56      0.65       402
           1       0.65      0.83      0.73       402

    accuracy                           0.69       804
   macro avg       0.71      0.69      0.69       804
weighted avg       0.71      0.69      0.69       804

Tested: n_clusters=10, k_neighbours=9, metric=<function euclidean at 0x123901e50> --> Acc: 0.7085
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.59      0.66       402
           1       0.66      0.80      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.69       804
weighted avg       0.71      0.70      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.76      0.58      0.66       402
           1       0.66      0.82      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.69       804
weighted avg       0.71      0.70      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.68      0.67       402
           1       0.67      0.64      0.65       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.69      0.67       402
           1       0.67      0.64      0.65       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.60      0.64       402
           1       0.64      0.72      0.68       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Tested: n_clusters=10, k_neighbours=9, metric=<function cityblock at 0x123913310> --> Acc: 0.6764
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.84      0.47      0.60       402
           1       0.63      0.91      0.75       402

    accuracy                           0.69       804
   macro avg       0.74      0.69      0.67       804
weighted avg       0.74      0.69      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.83      0.75       402
           1       0.78      0.62      0.69       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.79      0.64      0.71       402
           1       0.70      0.83      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.75      0.72       402
           1       0.72      0.66      0.69       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.60      0.67       402
           1       0.67      0.81      0.73       402

    accuracy                           0.70       804
   macro avg       0.71      0.70      0.70       804
weighted avg       0.71      0.70      0.70       804

Tested: n_clusters=10, k_neighbours=9, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.7107
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.74      0.70       402
           1       0.71      0.63      0.66       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.68       804
weighted avg       0.69      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.91      0.74       402
           1       0.83      0.45      0.58       402

    accuracy                           0.68       804
   macro avg       0.72      0.68      0.66       804
weighted avg       0.72      0.68      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.70      0.69       402
           1       0.69      0.67      0.68       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.67      0.68       402
           1       0.68      0.70      0.69       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.54      0.62       402
           1       0.63      0.80      0.71       402

    accuracy                           0.67       804
   macro avg       0.68      0.67      0.66       804
weighted avg       0.68      0.67      0.66       804

Tested: n_clusters=10, k_neighbours=11, metric=<function euclidean at 0x123901e50> --> Acc: 0.6796
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.69      0.67       402
           1       0.67      0.63      0.65       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.60      0.64       402
           1       0.64      0.71      0.67       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.70      0.69       402
           1       0.69      0.65      0.67       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.61      0.63       402
           1       0.64      0.69      0.66       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.61      0.61       402
           1       0.61      0.61      0.61       402

    accuracy                           0.61       804
   macro avg       0.61      0.61      0.61       804
weighted avg       0.61      0.61      0.61       804

Tested: n_clusters=10, k_neighbours=11, metric=<function cityblock at 0x123913310> --> Acc: 0.6517
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.75      0.71       402
           1       0.72      0.64      0.68       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.80      0.57      0.67       402
           1       0.67      0.86      0.75       402

    accuracy                           0.72       804
   macro avg       0.74      0.72      0.71       804
weighted avg       0.74      0.72      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.78      0.65      0.71       402
           1       0.70      0.82      0.76       402

    accuracy                           0.74       804
   macro avg       0.74      0.74      0.73       804
weighted avg       0.74      0.74      0.73       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.66      0.70       402
           1       0.69      0.77      0.73       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.71       804
weighted avg       0.72      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.69      0.70       402
           1       0.70      0.72      0.71       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Tested: n_clusters=10, k_neighbours=11, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.7134
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.72      0.72       402
           1       0.72      0.70      0.71       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.71       804
weighted avg       0.71      0.71      0.71       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.63      0.67       402
           1       0.67      0.76      0.71       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.77      0.72       402
           1       0.73      0.64      0.69       402

    accuracy                           0.71       804
   macro avg       0.71      0.71      0.70       804
weighted avg       0.71      0.71      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.50      0.60       402
           1       0.62      0.82      0.71       402

    accuracy                           0.66       804
   macro avg       0.68      0.66      0.65       804
weighted avg       0.68      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.67      0.67       402
           1       0.67      0.66      0.66       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Tested: n_clusters=10, k_neighbours=13, metric=<function euclidean at 0x123901e50> --> Acc: 0.6878
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.52      0.61       402
           1       0.63      0.80      0.70       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.66       804
weighted avg       0.67      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.63      0.65       402
           1       0.65      0.68      0.66       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.49      0.58       402
           1       0.61      0.81      0.70       402

    accuracy                           0.65       804
   macro avg       0.67      0.65      0.64       804
weighted avg       0.67      0.65      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.53      0.61       402
           1       0.63      0.80      0.70       402

    accuracy                           0.66       804
   macro avg       0.67      0.66      0.66       804
weighted avg       0.67      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.67      0.68       402
           1       0.68      0.69      0.69       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Tested: n_clusters=10, k_neighbours=13, metric=<function cityblock at 0x123913310> --> Acc: 0.6622
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.78      0.48      0.60       402
           1       0.63      0.87      0.73       402

    accuracy                           0.67       804
   macro avg       0.70      0.67      0.66       804
weighted avg       0.70      0.67      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.72      0.70       402
           1       0.70      0.65      0.68       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.72      0.71       402
           1       0.71      0.67      0.69       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.74      0.53      0.62       402
           1       0.63      0.81      0.71       402

    accuracy                           0.67       804
   macro avg       0.69      0.67      0.67       804
weighted avg       0.69      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.67      0.68       402
           1       0.68      0.70      0.69       402

    accuracy                           0.69       804
   macro avg       0.69      0.69      0.69       804
weighted avg       0.69      0.69      0.69       804

Tested: n_clusters=10, k_neighbours=13, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6836
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.75      0.70       402
           1       0.71      0.60      0.65       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.67       804
weighted avg       0.68      0.68      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.79      0.72       402
           1       0.73      0.59      0.66       402

    accuracy                           0.69       804
   macro avg       0.70      0.69      0.69       804
weighted avg       0.70      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.71      0.70       402
           1       0.70      0.69      0.70       402

    accuracy                           0.70       804
   macro avg       0.70      0.70      0.70       804
weighted avg       0.70      0.70      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.60      0.64       402
           1       0.64      0.73      0.68       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.84      0.72       402
           1       0.77      0.51      0.62       402

    accuracy                           0.68       804
   macro avg       0.70      0.68      0.67       804
weighted avg       0.70      0.68      0.67       804

Tested: n_clusters=10, k_neighbours=15, metric=<function euclidean at 0x123901e50> --> Acc: 0.6808
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.41      0.51       402
           1       0.58      0.81      0.68       402

    accuracy                           0.61       804
   macro avg       0.63      0.61      0.60       804
weighted avg       0.63      0.61      0.60       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.66      0.69      0.68       402
           1       0.68      0.65      0.66       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.78      0.69       402
           1       0.70      0.51      0.59       402

    accuracy                           0.65       804
   macro avg       0.66      0.65      0.64       804
weighted avg       0.66      0.65      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.63      0.64       402
           1       0.64      0.66      0.65       402

    accuracy                           0.65       804
   macro avg       0.65      0.65      0.65       804
weighted avg       0.65      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.61      0.62      0.62       402
           1       0.61      0.60      0.61       402

    accuracy                           0.61       804
   macro avg       0.61      0.61      0.61       804
weighted avg       0.61      0.61      0.61       804

Tested: n_clusters=10, k_neighbours=15, metric=<function cityblock at 0x123913310> --> Acc: 0.6376
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.80      0.50      0.62       402
           1       0.64      0.87      0.74       402

    accuracy                           0.69       804
   macro avg       0.72      0.69      0.68       804
weighted avg       0.72      0.69      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.71      0.69       402
           1       0.69      0.66      0.68       402

    accuracy                           0.68       804
   macro avg       0.68      0.68      0.68       804
weighted avg       0.68      0.68      0.68       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.80      0.74       402
           1       0.77      0.64      0.70       402

    accuracy                           0.72       804
   macro avg       0.73      0.72      0.72       804
weighted avg       0.73      0.72      0.72       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.73      0.67       402
           1       0.67      0.56      0.61       402

    accuracy                           0.64       804
   macro avg       0.65      0.64      0.64       804
weighted avg       0.65      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.75      0.49      0.59       402
           1       0.62      0.83      0.71       402

    accuracy                           0.66       804
   macro avg       0.68      0.66      0.65       804
weighted avg       0.68      0.66      0.65       804

Tested: n_clusters=10, k_neighbours=15, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6808
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.70      0.46      0.55       402
           1       0.60      0.81      0.69       402

    accuracy                           0.63       804
   macro avg       0.65      0.63      0.62       804
weighted avg       0.65      0.63      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.67      0.67       402
           1       0.67      0.66      0.67       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.78      0.57      0.66       402
           1       0.66      0.84      0.74       402

    accuracy                           0.71       804
   macro avg       0.72      0.71      0.70       804
weighted avg       0.72      0.71      0.70       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.54      0.92      0.68       402
           1       0.71      0.21      0.32       402

    accuracy                           0.56       804
   macro avg       0.62      0.56      0.50       804
weighted avg       0.62      0.56      0.50       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.59      0.54      0.56       402
           1       0.58      0.62      0.60       402

    accuracy                           0.58       804
   macro avg       0.58      0.58      0.58       804
weighted avg       0.58      0.58      0.58       804

Tested: n_clusters=10, k_neighbours=17, metric=<function euclidean at 0x123901e50> --> Acc: 0.6289
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.46      0.55       402
           1       0.59      0.79      0.68       402

    accuracy                           0.63       804
   macro avg       0.64      0.63      0.62       804
weighted avg       0.64      0.63      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.50      0.56       402
           1       0.59      0.73      0.65       402

    accuracy                           0.61       804
   macro avg       0.62      0.61      0.61       804
weighted avg       0.62      0.61      0.61       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.68      0.67      0.67       402
           1       0.67      0.68      0.68       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.53      0.59       402
           1       0.61      0.74      0.67       402

    accuracy                           0.63       804
   macro avg       0.64      0.63      0.63       804
weighted avg       0.64      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.60      0.68      0.64       402
           1       0.63      0.55      0.59       402

    accuracy                           0.61       804
   macro avg       0.62      0.61      0.61       804
weighted avg       0.62      0.61      0.61       804

Tested: n_clusters=10, k_neighbours=17, metric=<function cityblock at 0x123913310> --> Acc: 0.6318
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.43      0.54       402
           1       0.59      0.84      0.70       402

    accuracy                           0.63       804
   macro avg       0.66      0.63      0.62       804
weighted avg       0.66      0.63      0.62       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.78      0.69       402
           1       0.71      0.53      0.61       402

    accuracy                           0.65       804
   macro avg       0.66      0.65      0.65       804
weighted avg       0.66      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.76      0.71       402
           1       0.72      0.62      0.67       402

    accuracy                           0.69       804
   macro avg       0.70      0.69      0.69       804
weighted avg       0.70      0.69      0.69       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.75      0.68       402
           1       0.69      0.55      0.61       402

    accuracy                           0.65       804
   macro avg       0.66      0.65      0.65       804
weighted avg       0.66      0.65      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.61      0.65       402
           1       0.65      0.72      0.68       402

    accuracy                           0.67       804
   macro avg       0.67      0.67      0.67       804
weighted avg       0.67      0.67      0.67       804

Tested: n_clusters=10, k_neighbours=17, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6597
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.69      0.57      0.62       402
           1       0.63      0.74      0.68       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.65       804
weighted avg       0.66      0.66      0.65       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.72      0.36      0.48       402
           1       0.57      0.86      0.69       402

    accuracy                           0.61       804
   macro avg       0.65      0.61      0.59       804
weighted avg       0.65      0.61      0.59       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.56      0.76      0.65       402
           1       0.63      0.42      0.50       402

    accuracy                           0.59       804
   macro avg       0.60      0.59      0.57       804
weighted avg       0.60      0.59      0.57       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.58      0.82      0.68       402
           1       0.70      0.40      0.51       402

    accuracy                           0.61       804
   macro avg       0.64      0.61      0.60       804
weighted avg       0.64      0.61      0.60       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.56      0.87      0.68       402
           1       0.72      0.33      0.45       402

    accuracy                           0.60       804
   macro avg       0.64      0.60      0.57       804
weighted avg       0.64      0.60      0.57       804

Tested: n_clusters=10, k_neighbours=19, metric=<function euclidean at 0x123901e50> --> Acc: 0.6132
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.60      0.62       402
           1       0.62      0.65      0.64       402

    accuracy                           0.63       804
   macro avg       0.63      0.63      0.63       804
weighted avg       0.63      0.63      0.63       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.62      0.49      0.55       402
           1       0.58      0.70      0.63       402

    accuracy                           0.59       804
   macro avg       0.60      0.59      0.59       804
weighted avg       0.60      0.59      0.59       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.71      0.26      0.38       402
           1       0.55      0.89      0.68       402

    accuracy                           0.58       804
   macro avg       0.63      0.58      0.53       804
weighted avg       0.63      0.58      0.53       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.67      0.57      0.61       402
           1       0.62      0.71      0.67       402

    accuracy                           0.64       804
   macro avg       0.64      0.64      0.64       804
weighted avg       0.64      0.64      0.64       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.60      0.59      0.60       402
           1       0.60      0.61      0.61       402

    accuracy                           0.60       804
   macro avg       0.60      0.60      0.60       804
weighted avg       0.60      0.60      0.60       804

Tested: n_clusters=10, k_neighbours=19, metric=<function cityblock at 0x123913310> --> Acc: 0.6090
Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.65      0.70      0.67       402
           1       0.67      0.62      0.64       402

    accuracy                           0.66       804
   macro avg       0.66      0.66      0.66       804
weighted avg       0.66      0.66      0.66       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.73      0.39      0.51       402
           1       0.58      0.85      0.69       402

    accuracy                           0.62       804
   macro avg       0.66      0.62      0.60       804
weighted avg       0.66      0.62      0.60       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.64      0.81      0.72       402
           1       0.74      0.54      0.63       402

    accuracy                           0.68       804
   macro avg       0.69      0.68      0.67       804
weighted avg       0.69      0.68      0.67       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.54      0.91      0.68       402
           1       0.73      0.24      0.36       402

    accuracy                           0.57       804
   macro avg       0.64      0.57      0.52       804
weighted avg       0.64      0.57      0.52       804

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

           0       0.63      0.40      0.49       402
           1       0.56      0.77      0.65       402

    accuracy                           0.58       804
   macro avg       0.60      0.58      0.57       804
weighted avg       0.60      0.58      0.57       804

Tested: n_clusters=10, k_neighbours=19, metric=<function weighted_euclidean at 0x1346fd0d0> --> Acc: 0.6231

Best Params for dc_knn_centroid_nn: (8, 3, <function weighted_euclidean at 0x1346fd0d0>) → Accuracy: 0.7294

n_neighbour_scores = {3: 0.7405, 5: 0.7391, 7: 0.7418,
                      9: 0.7423, 11: 0.7505, 13: 0.7515,
                      15: 0.7547, 17: 0.7520, 19: 0.7522}

X = list(n_neighbour_scores.keys())
y= list(n_neighbour_scores.values())

plt.figure(figsize=(10, 6))
plt.plot(X, y, marker='o', linestyle='-', color='royalblue', linewidth=2, markersize=8)
plt.title("Effect of k on KNN Accuracy", fontsize=16)
plt.xlabel("Number of Neighbours (k)", fontsize=14)
plt.ylabel("Mean Accuracy", fontsize=14)
plt.xticks(X)
plt.grid(True, linestyle='--', alpha=0.6)

# Add score labels to each point
for i, (x, score) in enumerate(zip(X, y)):
    plt.text(x, score + 0.0005, f"{score:.4f}", ha='center', fontsize=11)

plt.tight_layout()
plt.show()

# Convert all_combos to a tidy DataFrame
results_df = pd.DataFrame([
    {
        "n_clusters": combo[0],
        "k_neighbours": combo[1],
        "metric": combo[2].__name__,
        "accuracy": acc
    }
    for combo, acc in all_combos_dc_knn.items()
])

# Make sure all values are floats, sort for better structure
results_df = results_df.sort_values(by=["metric", "n_clusters", "k_neighbours"])

# Create FacetGrid with better layout
g = sns.FacetGrid(
    results_df, col="metric", col_wrap=2, height=4.5,
    sharex=False, sharey=False, margin_titles=True
)

# Apply heatmaps cleanly to each subplot
def draw_heatmap(data, **kwargs):
    pivoted = data.pivot(index="n_clusters", columns="k_neighbours", values="accuracy")
    sns.heatmap(
        pivoted,
        annot=True,
        fmt=".3f",
        cmap="YlGnBu",
        linewidths=0.5,
        cbar=False,
        square=True
    )

g.map_dataframe(draw_heatmap)

# Formatting titles and layout
g.set_titles(col_template="{col_name}")
g.fig.subplots_adjust(top=0.9)
g.fig.suptitle("DC-KNN Accuracy across Parameter Combinations", fontsize=14)
plt.show()

# Convert all_combos to a tidy DataFrame
results_df = pd.DataFrame([
    {
        "n_clusters": combo[0],
        "k_neighbours": combo[1],
        "metric": combo[2].__name__,
        "accuracy": acc
    }
    for combo, acc in all_combos_dc_knn_cent.items()
])

# Make sure all values are floats, sort for better structure
results_df = results_df.sort_values(by=["metric", "n_clusters", "k_neighbours"])

# Create FacetGrid with better layout
g = sns.FacetGrid(
    results_df, col="metric", col_wrap=2, height=4.5,
    sharex=False, sharey=False, margin_titles=True
)

g.map_dataframe(draw_heatmap)

# Formatting titles and layout
g.set_titles(col_template="{col_name}")
g.fig.subplots_adjust(top=0.9)
g.fig.suptitle("Centroid DC-KNN Accuracy across Parameter Combinations", fontsize=14)
plt.show()

Ensemble DC-KNN with Random Forest

Hyperparameter Tune Random Forest

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV

param_grid_rf = {
    'n_estimators': [100, 200, 300],
    'max_depth': [10, 20, 30, None],
    'min_samples_split': [2, 5, 10],
    'class_weight': [None, 'balanced']
}

rf = RandomForestClassifier(random_state=42)

grid_search = GridSearchCV(
    estimator=rf,
    param_grid=param_grid_rf,
    cv=5,            # 5-fold cross-validation
    scoring='f1',     # Or 'accuracy', 'roc_auc', etc.
    n_jobs=-1,       # Use all CPU cores
    verbose=2
)

grid_search.fit(X_train_reduced, y_resampled)


best_rf = grid_search.best_estimator_
print("Best parameters:", grid_search.best_params_)

Fitting 5 folds for each of 72 candidates, totalling 360 fits
Best parameters: {'class_weight': None, 'max_depth': 30, 'min_samples_split': 2, 'n_estimators': 300}

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression

# === Train Best DC-KNN Model ===
def predict_dc_knn_proba(X_test_samples):
    """Wrapper to get DC-KNN probabilities in correct format for ensemble"""
    preds = dc_knn_combining_algorithm(
        X_train_reduced, y_resampled,
        X_test_samples, y_test.iloc[:len(X_test_samples)], 
        n_clusters_per_class=2,
        k=13,
        distance_metric=euclidean
    )
    # Convert predictions to probabilities [n_samples, 2]
    proba = np.zeros((len(preds), 2))
    proba[:, 1] = preds  # Binary classification where preds are 0/1
    proba[:, 0] = 1 - proba[:, 1]
    return proba

from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression

# === Train Best DC-KNN Centroid Model ===
def predict_dc_knn_centroid_proba(X_test_samples):
    """Wrapper to get DC-KNN probabilities in correct format for ensemble"""
    preds = dc_knn_centroid_nn(
        X_train_reduced, y_resampled,
        X_test_samples, y_test.iloc[:len(X_test_samples)], 
        n_clusters_per_class=8,
        k=3,
        distance_metric=weighted_euclidean
    )
    # Convert predictions to probabilities [n_samples, 2]
    proba = np.zeros((len(preds), 2))
    proba[:, 1] = preds  # Assuming binary classification where preds are 0/1
    proba[:, 0] = 1 - proba[:, 1]
    return proba

Hold-out test results

from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
def get_cm(y_test, y_pred, model):
    # Confusion Matrix for DC-KNN Standard + RF 
    cm = confusion_matrix(y_test, y_pred)
    disp_std = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=["Red Win", "Blue Win"])
    disp_std.plot(cmap="Reds")
    plt.title(f"{model} Confusion Matrix")
    plt.grid(False)
    plt.show()

Vanilla KNN

knn_pred = knn(X_train_reduced, y_resampled, X_test_reduced, y_test, n_neighbors=15)
knn_cm = get_cm(y_test, knn_pred, 'Vanilla KNN')

Baseline Model— Vanilla KNN
              precision    recall  f1-score   support

         0.0       0.79      0.66      0.72       503
         1.0       0.62      0.76      0.69       369

    accuracy                           0.70       872
   macro avg       0.71      0.71      0.70       872
weighted avg       0.72      0.70      0.71       872

Random Forest Classifier

# Evaluate on test set
# === Train Parameter Tuned Random Forest Classifier ===
rf = RandomForestClassifier(n_estimators=300, 
                            random_state=42, 
                            class_weight=None, 
                            max_depth=30,
                            min_samples_split=2,
    )

rf.fit(X_train_reduced, y_resampled)

rf_preds = rf.predict(X_test_reduced)
print(classification_report(y_test, rf_preds))
rf_cm = get_cm(y_test, rf_preds, model="Random Forest Classifier")

              precision    recall  f1-score   support

         0.0       0.80      0.82      0.81       503
         1.0       0.74      0.72      0.73       369

    accuracy                           0.78       872
   macro avg       0.77      0.77      0.77       872
weighted avg       0.78      0.78      0.78       872

Ensemble DC-KNN + RF

# Get DC-KNN probabilities for all test samples at once
dc_knn_proba = predict_dc_knn_proba(X_test_reduced)
rf_proba = rf.predict_proba(X_test_reduced)

# Stack predictions as features
X_stack = np.column_stack([dc_knn_proba[:, 1], rf_proba[:, 1]])  

# Train a meta-model with GradientBoostingClassifier
stacker_std = GradientBoostingClassifier()
stacker_std.fit(X_stack, y_test) 
ensemble_preds = stacker.predict(X_stack)

# CR 
print("DC-KNN + RF Ensemble Classification Report:")
print(classification_report(y_test, ensemble_preds))

# Confusion Matrix for DC-KNN Standard + RF 
cm_dc_knn_rf = get_cm(y_test, ensemble_preds, 'Ensemble DC-KNN + RF')

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

         0.0       0.79      0.68      0.73       503
         1.0       0.63      0.76      0.69       369

    accuracy                           0.71       872
   macro avg       0.71      0.72      0.71       872
weighted avg       0.73      0.71      0.71       872

DC-KNN + RF Ensemble Classification Report:
              precision    recall  f1-score   support

         0.0       0.84      0.81      0.82       503
         1.0       0.75      0.78      0.77       369

    accuracy                           0.80       872
   macro avg       0.79      0.80      0.80       872
weighted avg       0.80      0.80      0.80       872

Ensemble DC-KNN - Centroid Level + RF

# Get DC-KNN Centroid probabilities for all test samples at once
dc_knn_cent_proba = predict_dc_knn_centroid_proba(X_test_reduced)

# Stack predictions as features
X_stack_cent = np.column_stack([dc_knn_cent_proba[:, 1], rf_proba[:, 1]])  

stacker_cent = GradientBoostingClassifier()
stacker_cent.fit(X_stack_cent, y_test) 
ensemble_preds_cent = stacker.predict(X_stack_cent)

# CR
print("DC-KNN - Centroid Level + RF Ensemble Classification Report:")
print(classification_report(y_test, ensemble_preds_cent))

# Confusion Matrix for DC-KNN Standard
cm_dc_knn_cent_rf = get_cm(y_test, ensemble_preds_cent, 'Ensemble DC-KNN - Centroid Level + RF')

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

         0.0       0.79      0.68      0.73       503
         1.0       0.63      0.75      0.69       369

    accuracy                           0.71       872
   macro avg       0.71      0.72      0.71       872
weighted avg       0.72      0.71      0.71       872

DC-KNN - Centroid Level + RF Ensemble Classification Report:
              precision    recall  f1-score   support

         0.0       0.84      0.82      0.83       503
         1.0       0.76      0.79      0.78       369

    accuracy                           0.81       872
   macro avg       0.80      0.80      0.80       872
weighted avg       0.81      0.81      0.81       872

DC-KNN - Centroid Level

params_centroid = {"n_clusters_per_class": 8, "k": 3, "distance_metric": weighted_euclidean}

# Algorithm 3 – DC-KNN Centroid-Level
y_pred_centroid = dc_knn_centroid_nn(
    X_train_reduced, y_resampled, X_test_reduced, y_test,
    **params_centroid
)

# Confusion Matrix for DC-KNN Centroid-Level
cm_cent = get_cm(y_test, y_pred_centroid, 'DC-KNN - Centroid Level')

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

         0.0       0.79      0.68      0.73       503
         1.0       0.63      0.75      0.69       369

    accuracy                           0.71       872
   macro avg       0.71      0.71      0.71       872
weighted avg       0.72      0.71      0.71       872

DC-KNN

params_standard = {"n_clusters_per_class": 2, "k": 13, "distance_metric": euclidean}

# Algorithm 3 – DC-KNN Centroid-Level
y_pred_standard = dc_knn_combining_algorithm(
    X_train_reduced, y_resampled, X_test_reduced, y_test,
    **params_centroid
)
# Confusion Matrix for DC-KNN Centroid-Level
cm_standard = get_cm(y_test, y_pred_standard, 'Standard DC-KNN')

Algorithm 2 — DC-KNN with Standard Data-Level KNN
              precision    recall  f1-score   support

         0.0       0.74      0.68      0.71       503
         1.0       0.60      0.67      0.64       369

    accuracy                           0.68       872
   macro avg       0.67      0.68      0.67       872
weighted avg       0.68      0.68      0.68       872

Save DC-KNN Centroid + RF ensemble model

import joblib

joblib.dump(stacker_cent, 'ensemble_dc_knn_centroid_rf.pkl')
joblib.dump(rf, 'rf_model.pkl')
print("RF model saved as 'rf_model.pkl'")
print("Model saved as 'ensemble_dc_knn_centroid_rf.pkl'")

RF model saved as 'rf_model.pkl'
Model saved as 'ensemble_dc_knn_centroid_rf.pkl'

Load Models

rf = joblib.load('rf_model.pkl')
stacker_cent = joblib.load('ensemble_dc_knn_centroid_rf.pkl')

Predict on Last UFC card

df_upcoming = pd.read_csv("upcoming_preprocessed.csv")

# Drop the same unused and target columns used during training
X_upcoming = df_upcoming.drop(columns=["Winner", "RedFighter", "BlueFighter", "RedFighter_ID", "BlueFighter_ID", "source"], errors='ignore')

# Apply the same PCA
X_upcoming_reduced = pca.transform(X_upcoming)


# Get RF and DC-KNN centroid probabilities
rf_proba_upcoming = rf.predict_proba(X_upcoming_reduced)
dc_knn_cent_proba_upcoming = predict_dc_knn_centroid_proba(X_upcoming_reduced)

# Stack them in the same order as during training
X_stack_upcoming = np.column_stack([
    dc_knn_cent_proba_upcoming[:, 1], 
    rf_proba_upcoming[:, 1]
])

# Predict outcomes with confidence
upcoming_preds = stacker_cent.predict(X_stack_upcoming)
upcoming_proba = stacker_cent.predict_proba(X_stack_upcoming)


#Ignore this output

Algorithm 3 — DC-KNN with Centroid-Level Nearest Neighbors
              precision    recall  f1-score   support

         0.0       0.43      0.50      0.46         6
         1.0       0.25      0.20      0.22         5

    accuracy                           0.36        11
   macro avg       0.34      0.35      0.34        11
weighted avg       0.35      0.36      0.35        11

df_upcoming['PredictedWinner_DCKNN_CENT'] = upcoming_preds
df_upcoming['PredictedWinner_DCKNN_CENT'] = df_upcoming['PredictedWinner_DCKNN_CENT'].map({0: 'Red', 1: 'Blue'})
df_upcoming['ModelConfidence'] = (upcoming_proba.max(axis=1) * 100).round(2)

df_upcoming['WinnerCorner'] = df_upcoming['Winner'].map({0: 'Red', 1: 'Blue'})

# Calculate accuracy
accuracy_cent = accuracy_score(df_upcoming['Winner'], upcoming_preds)
print(f"DC-KNN CENTROID Prediction Accuracy on Upcoming Card: {accuracy_cent:.2%}")

DC-KNN CENTROID Prediction Accuracy on Upcoming Card: 72.73%

df_upcoming[['RedFighter', 'BlueFighter','PredictedWinner_DCKNN_CENT', 'ModelConfidence', 'WinnerCorner']]

	RedFighter	BlueFighter	PredictedWinner_DCKNN_CENT	ModelConfidence	WinnerCorner
0	Colby Covington	Joaquin Buckley	Blue	65.54	Blue
1	Cub Swanson	Billy Quarantillo	Red	73.23	Red
2	Manel Kape	Bruno Silva	Red	98.45	Red
3	Vitor Petrino	Dustin Jacoby	Red	82.98	Blue
4	Adrian Yanez	Daniel Marcos	Blue	50.96	Blue
5	Navajo Stirling	Tuco Tokkos	Red	98.55	Red
6	Michael Johnson	Ottman Azaitar	Red	77.14	Red
7	Joel Alvarez	Drakkar Klose	Red	91.34	Red
8	Sean Woodson	Fernando Padilla	Red	98.45	Red
9	Miles Johns	Felipe Lima	Red	94.50	Blue
10	Davey Grant	Ramon Taveras	Blue	67.05	Red